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NVI
DI
A,
a
n
d
Dee
p
Seek
f
r
o
m
h
ig
h
-
f
ly
er
.
Fig
u
r
e
1
.
C
o
m
p
o
n
en
ts
o
f
L
L
M
in
o
r
d
e
r
o
f
t
h
eir
u
s
ag
e
T
h
e
cu
r
r
en
t
s
tate
-
of
-
th
e
-
ar
t
in
L
L
Ms
is
m
ar
k
ed
b
y
m
o
d
els
li
k
e
GPT
-
4
,
PaL
M
2
,
L
L
aM
A,
Dee
p
Seek
,
an
d
Gem
in
i,
wh
ich
s
h
o
wca
s
e
b
r
ea
k
th
r
o
u
g
h
s
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r
al
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u
ag
e
u
n
d
er
s
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in
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d
g
e
n
er
atio
n
.
T
h
ese
m
o
d
els
h
av
e
b
ec
o
m
e
h
ig
h
l
y
p
r
o
f
icie
n
t
in
task
s
s
u
ch
as
tex
t
g
en
e
r
atio
n
,
tr
a
n
s
latio
n
,
s
u
m
m
a
r
iz
atio
n
,
an
d
c
o
m
p
lex
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ea
s
o
n
in
g
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p
Seek
is
a
s
p
e
cialize
d
m
o
d
el
f
o
r
en
h
a
n
ce
d
s
ea
r
ch
ca
p
ab
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o
f
f
er
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g
m
o
r
e
co
n
te
x
t
-
awa
r
e
an
d
r
elev
a
n
t
r
esp
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es
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s
e
ar
ch
q
u
e
r
ies.
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i,
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ev
el
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ed
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y
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Dee
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Min
d
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r
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u
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g
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els
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al
ca
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ab
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ies,
h
an
d
lin
g
b
o
th
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v
is
u
al
in
p
u
ts
to
d
eliv
er
h
ig
h
ly
ac
cu
r
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e
r
esp
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n
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es
ac
r
o
s
s
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iv
er
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.
Ad
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itio
n
ally
,
m
o
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els
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r
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led
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tr
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n
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o
f
p
ar
am
eter
s
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im
p
r
o
v
in
g
p
er
f
o
r
m
an
ce
with
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ewe
r
r
eso
u
r
ce
s
.
T
h
e
in
teg
r
atio
n
o
f
r
ein
f
o
r
ce
m
en
t
lear
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in
g
f
r
o
m
h
u
m
an
f
ee
d
b
ac
k
(
R
L
HF)
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h
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ce
s
r
eliab
ilit
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an
d
eth
ic
al
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af
eg
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ar
d
s
.
R
esear
ch
is
also
f
o
c
u
s
ed
o
n
im
p
r
o
v
in
g
th
e
m
o
d
els'
s
af
ety
,
b
ias
r
ed
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ctio
n
,
an
d
i
n
ter
ac
tiv
ity
,
p
a
v
in
g
th
e
wa
y
f
o
r
m
o
r
e
v
er
s
atile
an
d
r
esp
o
n
s
ib
le
AI
to
o
ls
.
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y
T
h
e
in
tr
o
d
u
ctio
n
o
f
t
r
an
s
f
o
r
m
er
ar
ch
itectu
r
e
h
as
b
ee
n
f
o
u
n
d
atio
n
al
f
o
r
m
a
n
y
s
u
b
s
eq
u
en
t
L
L
Ms
[
1
]
.
Am
o
n
g
t
h
ese,
B
E
R
T
is
a
wid
ely
u
s
ed
L
L
M
ar
c
h
itectu
r
e
t
h
at
s
ig
n
if
ican
tly
a
d
v
an
ce
s
th
e
s
tate
-
of
-
th
e
-
ar
t
in
n
atu
r
al
lan
g
u
a
g
e
u
n
d
e
r
s
tan
d
i
n
g
task
s
[
2
]
.
R
ad
f
o
r
d
et
a
l
.
[
3
]
in
tr
o
d
u
ce
th
e
GPT
ar
ch
itectu
r
e,
wh
ich
d
em
o
n
s
tr
ates
th
e
ef
f
ec
tiv
en
ess
o
f
au
to
r
eg
r
ess
iv
e
lan
g
u
ag
e
m
o
d
elin
g
f
o
r
g
en
er
at
in
g
co
h
e
r
en
t
tex
t.
R
ad
f
o
r
d
et
a
l
.
[
4
]
also
p
r
ese
n
t
th
e
m
o
r
e
ef
f
icien
t
GPT2
m
o
d
el,
h
i
g
h
lig
h
tin
g
its
lar
g
e
s
ca
le
an
d
ab
ilit
y
to
p
er
f
o
r
m
a
wid
e
r
an
g
e
o
f
la
n
g
u
ag
e
task
s
.
Yan
g
et
a
l
.
[
5
]
p
r
o
p
o
s
e
XL
Net,
a
n
o
v
el
au
to
r
eg
r
ess
iv
e
lan
g
u
a
g
e
m
o
d
el
th
at
o
v
e
r
co
m
es
lim
itat
io
n
s
o
f
B
E
R
T
b
y
lev
e
r
ag
in
g
p
er
m
u
tatio
n
s
d
u
r
in
g
p
r
e
-
tr
ain
in
g
an
d
in
teg
r
atin
g
id
ea
s
f
r
o
m
t
r
an
s
f
o
r
m
er
-
XL
.
L
iu
et
a
l
.
[
6
]
in
t
r
o
d
u
ce
R
o
B
E
R
T
a,
with
im
p
r
o
v
ed
p
er
f
o
r
m
an
ce
o
v
er
B
E
R
T
b
y
o
p
tim
izin
g
tr
ain
in
g
h
y
p
e
r
-
p
a
r
am
eter
s
an
d
u
s
in
g
lar
g
er
d
atasets
.
Su
n
et
a
l
.
[
7
]
p
r
esen
t
E
R
NI
E
2
.
0
,
wh
ich
ex
ten
d
s
B
E
R
T
with
a
co
n
tin
u
al
p
r
e
-
tr
ain
in
g
f
r
a
m
ewo
r
k
t
o
a
d
ap
t
to
n
ew
task
s
an
d
d
o
m
ain
s
.
Kesk
ar
et
a
l
.
[
8
]
p
r
ese
n
t
c
o
n
d
iti
o
n
al
t
r
a
n
s
f
o
r
m
e
r
l
a
n
g
u
ag
e
(
C
T
R
L
)
,
a
co
n
d
it
io
n
al
l
an
g
u
a
g
e
m
o
d
e
l
d
es
ig
n
ed
f
o
r
c
o
n
t
r
o
lla
b
l
e
te
x
t
g
e
n
e
r
a
ti
o
n
b
y
a
n
al
y
zi
n
g
l
a
r
g
e
v
o
l
u
m
es
o
f
d
ata
u
s
i
n
g
m
o
d
el
-
b
as
ed
s
o
u
r
ce
a
tt
r
i
b
u
ti
o
n
.
R
a
f
f
el
et
a
l
.
[
9
]
p
r
o
p
o
s
e
T
5
,
w
h
ic
h
u
ti
liz
es
t
r
a
n
s
f
er
le
ar
n
i
n
g
a
n
d
f
r
a
m
es
a
ll
n
at
u
r
al
l
an
g
u
a
g
e
p
r
o
ce
s
s
in
g
(
NL
P)
t
ask
s
as
te
x
t
-
to
-
te
x
t
pr
o
b
l
em
s
,
r
esu
lti
n
g
i
n
a
ch
ie
v
i
n
g
s
t
at
e
-
of
-
th
e
-
ar
t
r
es
u
l
ts
o
n
a
w
id
e
r
an
g
e
o
f
b
e
n
ch
m
a
r
k
s
.
R
ai
aa
n
et
a
l
.
[1
0]
p
r
o
v
i
d
e
a
co
m
p
r
e
h
e
n
s
i
v
e
o
v
e
r
v
ie
w
o
f
c
u
r
r
e
n
t
L
L
Ms
.
G
e
et
a
l
.
[
1
1
]
i
n
t
r
o
d
u
ce
O
p
en
AGI
f
o
r
r
ea
l
-
w
o
r
l
d
t
ask
s
.
Hu
a
n
g
e
t
a
l.
[
1
2
]
p
r
es
en
t
d
o
m
ain
s
p
ec
i
f
i
c
q
u
esti
o
n
a
n
s
w
er
in
g
l
an
g
u
a
g
e
m
o
d
el
(
DS
QA
-
L
L
M)
f
o
r
in
f
o
r
m
a
ti
v
e
E
m
be
dding
L
a
y
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r
P
os
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ti
on
a
l
E
n
c
odi
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A
tt
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on
M
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c
h
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s
m
F
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-
F
or
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r
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Ne
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r
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twor
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y
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Nor
m
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t
io
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R
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du
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C
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De
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ode
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Ou
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L
ay
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Obje
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ti
v
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s
P
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tr
a
i
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a
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d
F
i
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-
tu
n
i
ng
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
2
5
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tell
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14
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d
o
m
ai
n
-
s
p
e
ci
f
ic
q
u
e
r
i
es.
Si
m
i
lar
ly
,
Si
p
i
o
e
t
a
l
.
[
1
3
]
e
x
p
l
o
r
e
t
h
e
r
o
le
o
f
L
L
Ms
i
n
t
h
e
ex
tr
ac
ti
o
n
o
f
l
a
n
g
u
a
g
e
s
em
a
n
t
ics
.
Ho
ltz
m
a
n
e
t
a
l
.
[
1
4
]
i
n
v
esti
g
ate
is
s
u
es
r
elate
d
to
d
eg
en
er
ate
tex
t
g
en
e
r
atio
n
in
au
t
o
r
eg
r
ess
iv
e
lan
g
u
ag
e
m
o
d
els
an
d
p
r
o
p
o
s
e
s
tr
ateg
ies
to
m
itig
ate
it.
B
o
d
o
r
et
a
l.
[
1
5
]
lev
e
r
ag
es
L
L
Ms
f
o
r
d
ata
en
r
ich
m
en
t
an
d
m
o
n
ito
r
in
g
to
war
d
s
p
e
r
f
o
r
m
an
ce
o
p
tim
izatio
n
.
L
ewis
et
a
l
.
[
1
6
]
in
tr
o
d
u
ce
b
i
d
ir
ec
tio
n
al
an
d
au
t
o
-
r
eg
r
ess
iv
e
tr
an
s
f
o
r
m
e
r
s
(
B
AR
T
)
,
a
s
eq
u
en
ce
-
to
-
s
eq
u
en
ce
m
o
d
el
p
r
e
-
tr
ain
ed
b
y
d
e
-
n
o
is
in
g
co
r
r
u
p
ted
tex
t.
B
r
o
wn
et
a
l
.
[
1
7
]
d
em
o
n
s
tr
ate
t
h
e
f
ew
-
s
h
o
t le
ar
n
in
g
ca
p
ab
ilit
ies
o
f
GPT3
,
s
h
o
win
g
its
ab
ilit
y
to
p
e
r
f
o
r
m
d
i
v
er
s
e
task
s
with
m
in
im
al
task
-
s
p
ec
if
ic
t
r
ain
in
g
d
ata
.
T
h
e
tech
n
ical
p
ap
er
"GPT
-
4
tech
n
ical
r
ep
o
r
t
"
b
y
Op
e
n
AI
[
1
8
]
p
r
o
v
id
es
a
co
m
p
r
eh
e
n
s
iv
e
o
v
er
v
iew
o
f
GPT
-
4
'
s
d
ev
elo
p
m
en
t
an
d
ca
p
ab
ilit
ies.
L
an
et
a
l.
[
1
9
]
in
tr
o
d
u
ce
AL
B
E
R
T
,
a
p
ar
am
eter
-
r
ed
u
c
tio
n
tech
n
iq
u
e
f
o
r
B
E
R
T
th
at
m
ain
tain
s
co
m
p
etitiv
e
p
er
f
o
r
m
a
n
ce
wh
ile
r
ed
u
ci
n
g
m
o
d
el
s
ize.
On
th
e
o
th
er
h
an
d
,
Xie
et
a
l
.
[
2
0
]
p
r
o
p
o
s
e
a
m
eth
o
d
f
o
r
u
n
s
u
p
er
v
is
ed
d
ata
au
g
m
en
tatio
n
to
im
p
r
o
v
e
th
e
r
o
b
u
s
tn
ess
an
d
g
en
er
aliza
tio
n
o
f
lan
g
u
ag
e
m
o
d
els.
R
ad
f
o
r
d
et
a
l
.
[
2
1
]
ex
p
lo
r
e
th
e
u
s
e
o
f
n
atu
r
al
lan
g
u
ag
e
s
u
p
e
r
v
is
io
n
to
p
r
e
-
tr
ai
n
v
is
io
n
tr
an
s
f
o
r
m
er
s
,
h
ig
h
lig
h
tin
g
th
e
p
o
ten
tial
o
f
cr
o
s
s
-
m
o
d
al
lea
r
n
in
g
.
L
in
et
a
l
.
[
2
2
]
s
u
r
v
ey
d
e
-
b
iasi
n
g
tech
n
iq
u
es
f
o
r
lan
g
u
ag
e
m
o
d
els an
d
ev
al
u
ate
th
eir
ef
f
ec
tiv
en
ess
o
n
v
a
r
io
u
s
b
en
ch
m
ar
k
s
.
Sch
ö
lk
o
p
f
e
t a
l.
[
2
3
]
d
is
cu
s
s
th
e
im
p
o
r
tan
ce
o
f
ca
u
s
ality
in
r
e
p
r
esen
tatio
n
lear
n
i
n
g
an
d
its
im
p
licatio
n
s
f
o
r
b
u
ild
in
g
m
o
r
e
in
ter
p
r
etab
le
a
n
d
r
eliab
le
lan
g
u
a
g
e
m
o
d
els.
B
en
d
er
et
a
l
.
[
2
4
]
d
is
cu
s
s
eth
ical
ch
allen
g
es
r
elate
d
to
NL
P
,
in
clu
d
in
g
b
ias,
f
air
n
ess
,
an
d
r
esp
o
n
s
ib
le
AI
d
ev
elo
p
m
en
t,
wh
ich
ar
e
r
elev
a
n
t
to
L
L
Ms.
S
u
n
et
a
l
.
[
2
5
]
r
e
v
ie
w
tec
h
n
i
q
u
es
f
o
r
m
iti
g
ati
n
g
g
e
n
d
e
r
b
ias
i
n
NL
P
tas
k
s
,
in
cl
u
d
in
g
t
h
o
s
e
i
n
v
o
l
v
i
n
g
LLMs
.
B
a
k
k
e
r
et
a
l
.
[
2
6
]
p
r
es
en
t
a
m
e
th
o
d
f
o
r
f
i
n
e
-
t
u
n
i
n
g
l
an
g
u
a
g
e
m
o
d
e
ls
u
s
in
g
h
u
m
a
n
f
e
e
d
b
ac
k
,
ad
d
r
ess
i
n
g
c
h
all
e
n
g
es
r
e
lat
e
d
t
o
c
o
n
tr
o
l
la
b
il
it
y
an
d
ali
g
n
m
e
n
t
wit
h
u
s
er
p
r
e
f
e
r
e
n
c
es.
Di
n
g
et
a
l
.
[
2
7
]
d
is
cu
s
s
v
ar
io
u
s
asp
ec
ts
o
f
L
L
Ms,
in
cl
u
d
in
g
th
eir
u
s
e
in
s
p
ec
ialized
d
o
m
ain
s
an
d
th
e
ch
allen
g
es
o
f
u
n
d
e
r
s
tan
d
in
g
d
o
m
ain
-
s
p
ec
if
ic
te
r
m
in
o
lo
g
y
.
W
eiss
et
a
l.
[
2
8
]
d
is
cu
s
s
th
e
v
ar
io
u
s
tr
an
s
f
er
le
ar
n
in
g
tech
n
iq
u
es
with
ca
s
e
s
tu
d
ies.
Gu
o
et
a
l
.
[
2
9
]
in
tr
o
d
u
ce
Dee
p
Seek
-
R
1
,
wh
ich
in
co
r
p
o
r
ates
m
u
lti
-
s
tag
e
tr
ain
in
g
an
d
c
o
ld
-
s
tar
t
d
ata
b
ef
o
r
e
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
.
T
h
is
p
ap
er
also
d
em
o
n
s
tr
ates
th
at
th
e
r
ea
s
o
n
in
g
p
atter
n
s
o
f
lar
g
er
m
o
d
els
ca
n
b
e
d
is
till
ed
in
to
s
m
all
er
m
o
d
els,
th
er
eb
y
r
ed
u
cin
g
th
e
r
e
q
u
ir
em
en
ts
in
t
er
m
s
o
f
co
m
p
u
tin
g
r
eso
u
r
ce
s
.
3.
CO
NCERN
S AN
D
CH
A
L
L
E
NG
E
S
On
e
m
ajo
r
co
n
ce
r
n
in
L
L
Ms
i
s
ab
o
u
t
th
e
p
o
ten
tial
m
is
u
s
e
o
f
L
L
Ms
f
o
r
g
en
e
r
atin
g
h
a
r
m
f
u
l
co
n
ten
t
s
u
ch
as
m
is
in
f
o
r
m
atio
n
,
h
ate
s
p
ee
ch
,
o
r
d
ee
p
-
f
ak
e
tex
t.
A
s
p
ec
if
ic
ex
am
p
le
o
f
h
ar
m
f
u
l
m
is
u
s
e
o
f
an
L
L
M
in
v
o
lv
es
th
e
2
0
2
0
in
cid
e
n
t
wh
er
e
a
d
ee
p
f
ak
e
tex
t
g
en
er
ato
r
was
u
s
ed
to
cr
ea
te
a
f
a
k
e
in
ter
v
iew
with
a
p
r
o
m
in
e
n
t
p
o
liti
ca
l
lead
er
.
E
n
s
u
r
in
g
r
esp
o
n
s
ib
le
u
s
e
an
d
m
itig
atin
g
h
ar
m
f
u
l
ap
p
licatio
n
s
is
a
s
ig
n
if
ican
t
ch
allen
g
e.
L
L
Ms
ca
n
in
ad
v
er
t
en
tly
p
er
p
etu
ate
o
r
am
p
lify
b
i
ases
p
r
esen
t
in
th
e
d
ata
th
ey
a
r
e
tr
ain
ed
o
n
.
T
h
is
b
ias
ca
n
m
an
if
est
in
v
ar
io
u
s
f
o
r
m
s
s
u
ch
as
g
en
d
er
,
r
a
cial,
o
r
cu
ltu
r
al
b
iases
,
lead
in
g
to
u
n
f
air
o
r
d
is
cr
im
in
ato
r
y
o
u
tp
u
ts
[
3
0
]
.
A
s
p
ec
if
ic
ex
am
p
le
o
f
L
L
Ms
p
er
p
etu
atin
g
b
ias
o
cc
u
r
r
e
d
with
a
m
o
d
el
u
s
ed
f
o
r
r
ec
r
u
itm
en
t
a
n
d
h
ir
in
g
,
wh
e
r
e
th
e
AI
was
tr
ain
ed
o
n
h
is
t
o
r
ical
d
ata
o
f
p
ast
h
ir
in
g
d
ec
is
io
n
s
,
wh
ich
wer
e
alr
ea
d
y
in
f
lu
e
n
ce
d
b
y
b
ias.
I
n
th
is
ca
s
e,
th
e
m
o
d
el
ex
h
ib
i
ted
g
en
d
er
b
ias,
f
av
o
r
in
g
m
a
le
ca
n
d
id
ates
o
v
er
f
em
ale
ca
n
d
id
ates
f
o
r
tech
n
ical
r
o
les,
ev
en
th
o
u
g
h
b
o
t
h
g
en
d
er
s
h
ad
s
im
ilar
q
u
alif
icati
o
n
s
.
L
L
Ms
ca
n
b
e
ex
p
lo
ited
to
g
en
e
r
ate
m
alicio
u
s
co
n
ten
t,
s
u
ch
as
p
h
is
h
in
g
em
ails
,
f
ak
e
n
ews
ar
ticles,
o
r
e
v
en
m
alwa
r
e.
L
L
Ms
ca
n
in
f
r
in
g
e
o
n
u
s
er
p
r
iv
ac
y
,
esp
ec
ially
in
ca
s
es
wh
er
e
th
e
y
ar
e
tr
ain
e
d
o
n
s
en
s
itiv
e
o
r
p
er
s
o
n
al
d
ata.
T
h
er
e
ar
e
co
n
ce
r
n
s
a
b
o
u
t
th
e
p
r
i
v
a
cy
im
p
licatio
n
s
o
f
g
en
er
atin
g
tex
t
th
at
m
ay
in
ad
v
er
ten
tly
r
ev
ea
l
co
n
f
id
e
n
tial
in
f
o
r
m
atio
n
o
r
co
m
p
r
o
m
is
e
u
s
er
p
r
iv
ac
y
[
3
1
]
.
I
n
o
n
e
ca
s
e,
u
s
er
s
n
o
ticed
th
at
wh
en
ask
ed
ab
o
u
t
p
r
i
v
ate
d
etails,
lik
e
p
er
s
o
n
al
m
ed
ical
h
is
to
r
ies
o
r
co
n
v
er
s
atio
n
s
th
at
wer
e
s
h
ar
ed
with
AI
m
o
d
els
in
ea
r
lier
v
e
r
s
io
n
s
,
th
e
m
o
d
el
s
o
m
etim
es
p
r
o
d
u
c
ed
o
u
tp
u
ts
th
at
s
ee
m
ed
to
r
e
ca
ll
s
p
ec
if
ic
d
etails
-
in
f
o
r
m
ati
o
n
th
at
was
n
e
v
er
d
ir
ec
tly
p
r
o
v
id
ed
i
n
th
e
q
u
er
y
.
T
r
ain
in
g
L
L
Ms r
e
q
u
ir
es si
g
n
i
f
ican
t c
o
m
p
u
tatio
n
al
r
eso
u
r
ce
s
,
wh
ich
ca
n
h
av
e
a
co
n
s
id
er
ab
le
e
n
v
ir
o
n
m
en
tal
i
m
p
ac
t
in
ter
m
s
o
f
en
e
r
g
y
co
n
s
u
m
p
tio
n
an
d
ca
r
b
o
n
e
m
is
s
i
o
n
s
.
Un
d
e
r
s
tan
d
in
g
an
d
in
ter
p
r
etin
g
th
e
o
u
tp
u
ts
o
f
L
L
Ms
ca
n
b
e
c
h
allen
g
in
g
d
u
e
to
t
h
eir
co
m
p
lex
ity
an
d
la
ck
o
f
tr
an
s
p
ar
e
n
cy
.
L
L
Ms
also
lack
in
ex
p
lain
ab
il
ity
.
Fo
r
in
s
tan
ce
,
wh
ile
t
h
e
m
o
d
el
m
ay
p
r
o
v
id
e
a
r
ejec
tio
n
d
ec
is
io
n
,
it
d
o
esn
'
t
o
f
f
er
clea
r
r
ea
s
o
n
in
g
b
e
h
in
d
wh
y
o
n
e
lo
an
a
p
p
lican
t
is
ap
p
r
o
v
e
d
an
d
an
o
t
h
er
is
d
en
ie
d
,
esp
ec
ially
if
b
o
th
ap
p
lican
ts
h
av
e
s
im
ilar
f
in
an
cial
p
r
o
f
iles
.
Fin
ally
,
L
L
Ms
m
ay
s
tr
u
g
g
le
with
u
n
d
er
s
tan
d
in
g
d
o
m
ain
-
s
p
ec
if
ic
k
n
o
wled
g
e
o
r
co
n
tex
t,
lead
in
g
to
in
ac
cu
r
ac
ies
o
r
ir
r
elev
an
t
o
u
tp
u
ts
in
ce
r
tain
d
o
m
ain
s
.
T
h
e
d
etails
o
f
th
is
last
ch
allen
g
e
ar
e
e
x
p
lain
ed
in
th
e
s
u
b
s
eq
u
en
t sectio
n
s
.
4.
DO
M
AIN
-
SPEC
I
F
I
C
K
NO
WL
E
DG
E
AN
D
CO
NT
E
XT
Do
m
ain
-
s
p
ec
if
ic
k
n
o
wled
g
e
a
n
d
co
n
te
x
t p
o
s
e
s
ig
n
if
ic
an
t c
h
allen
g
es f
o
r
LLMs
d
u
e
to
t
h
eir
g
en
er
alis
t
n
atu
r
e.
I
n
o
n
e
ca
s
e,
an
L
L
M
was
u
s
ed
in
a
clin
ical
s
ettin
g
to
s
u
g
g
est
tr
ea
tm
en
ts
f
o
r
a
p
atien
t
with
a
r
a
r
e
au
to
im
m
u
n
e
d
is
ea
s
e,
b
u
t
it
r
e
co
m
m
en
d
e
d
a
s
tan
d
a
r
d
tr
ea
t
m
en
t
f
o
r
m
o
r
e
co
m
m
o
n
c
o
n
d
itio
n
s
,
ig
n
o
r
in
g
th
e
n
u
an
ce
d
,
ev
i
d
en
ce
-
b
ased
p
r
o
t
o
co
ls
r
eq
u
ir
ed
f
o
r
th
at
s
p
ec
if
i
c
d
is
o
r
d
er
.
T
h
ese
ch
allen
g
es
d
er
iv
e
f
r
o
m
s
ev
er
al
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ir
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n
Ma
ye
e
A
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v
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la
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2571
m
ajo
r
f
ac
to
r
s
,
in
clu
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o
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ain
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p
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tis
e,
u
n
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er
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tan
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p
ec
ialized
ter
m
i
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o
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co
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tex
tu
al
u
n
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er
s
tan
d
i
n
g
,
d
ata
b
ias,
an
d
tr
an
s
f
er
lear
n
in
g
lim
itatio
n
s
.
S
o
m
e
s
am
p
le
o
u
tp
u
ts
f
r
o
m
p
o
p
u
lar
L
L
Ms
s
u
ch
as
C
h
atGPT
4
.
0
,
So
n
n
et,
B
E
R
T
,
an
d
L
lam
a
ar
e
p
r
esen
ted
in
T
a
b
le
1
.
T
h
ese
f
ac
to
r
s
an
d
s
o
m
e
p
o
s
s
ib
le
s
o
lu
tio
n
s
ar
e
d
is
cu
s
s
ed
n
ex
t.
T
ab
le
1
.
Sam
p
le
o
u
tp
u
ts
f
r
o
m
p
o
p
u
lar
L
L
Ms sh
o
wca
s
in
g
d
o
m
ain
-
s
p
ec
if
ic
ch
allen
g
es
LLM
_
N
a
m
e
O
u
t
p
u
t
D
o
ma
i
n
C
h
a
l
l
e
n
g
e
PT
-
4o
Th
e
c
o
mm
o
n
t
r
e
a
t
m
e
n
t
f
o
r
n
e
u
r
o
f
i
b
r
o
ma
t
o
s
i
s
i
n
c
l
u
d
e
s
u
si
n
g
p
e
n
i
c
i
l
l
i
n
.
M
e
d
i
c
a
l
I
n
c
o
r
r
e
c
t
r
e
c
o
m
me
n
d
a
t
i
o
n
:
P
e
n
i
c
i
l
l
i
n
i
s n
o
t
a
t
r
e
a
t
me
n
t
f
o
r
n
e
u
r
o
f
i
b
r
o
mat
o
s
i
s,
h
i
g
h
l
i
g
h
t
i
n
g
t
h
e
m
o
d
e
l
's
l
a
c
k
o
f
d
o
ma
i
n
-
s
p
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se
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B
ER
T
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c
h
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r
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c
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s.
Li
t
e
r
a
t
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r
e
M
i
s
l
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a
d
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n
g
:
S
h
e
r
l
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l
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p
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b
a
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k
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v
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C
l
a
u
d
e
3
.
5
I
n
t
h
e
f
i
e
l
d
o
f
q
u
a
n
t
u
m
me
c
h
a
n
i
c
s,
t
h
e
S
c
h
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d
i
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e
r
's
c
a
t
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p
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m
e
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t
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a
s
d
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si
g
n
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d
t
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a
n
e
l
e
c
t
r
o
n
c
a
n
b
e
b
o
t
h
a
l
i
v
e
a
n
d
d
e
a
d
a
t
t
h
e
sam
e
t
i
me
.
P
h
y
s
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c
s
I
n
c
o
r
r
e
c
t
e
x
p
l
a
n
a
t
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:
S
c
h
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d
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s
c
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me
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d
t
o
q
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m
su
p
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p
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si
t
i
o
n
,
n
o
t
e
l
e
c
t
r
o
n
s
t
a
t
e
s.
G
P
T
-
4
I
n
t
h
e
mo
v
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e
T
h
e
P
h
a
n
t
o
m
H
o
r
i
z
o
n
(
1
9
9
5
)
,
t
h
e
l
e
a
d
r
o
l
e
w
a
s
p
l
a
y
e
d
b
y
Jo
h
n
D
o
e
.
En
t
e
r
t
a
i
n
me
n
t
H
a
l
l
u
c
i
n
a
t
i
o
n
:
Th
e
P
h
a
n
t
o
m
H
o
r
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s
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o
t
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d
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o
h
n
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sso
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d
w
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h
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t
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P
a
LM
2
Th
e
sq
u
a
r
e
r
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o
t
o
f
6
4
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s
8
,
a
n
d
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h
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c
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p
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t
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G
e
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man
y
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s
B
e
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l
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n
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u
t
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h
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Ei
f
f
e
l
To
w
e
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i
s
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n
Lo
n
d
o
n
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G
e
o
g
r
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p
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c
o
r
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t
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f
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ma
t
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h
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Ei
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t
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M
A
Th
e
t
r
e
a
t
me
n
t
f
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r
Ty
p
e
2
D
i
a
b
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t
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o
f
t
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n
v
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l
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c
t
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d
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sp
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h
d
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a
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d
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me
d
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c
a
t
i
o
n
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M
e
d
i
c
a
l
M
i
s
l
e
a
d
i
n
g
:
I
n
su
l
i
n
i
s
p
r
i
mar
i
l
y
u
sed
f
o
r
Ty
p
e
1
D
i
a
b
e
t
e
s
,
n
o
t
a
s
a
f
i
r
st
-
l
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n
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t
m
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t
f
o
r
T
y
p
e
2
.
S
o
n
n
e
t
To
f
i
n
d
a
d
o
c
u
m
e
n
t
r
e
l
a
t
e
d
t
o
1
9
th
-
c
e
n
t
u
r
y
l
i
t
e
r
a
t
u
r
e
,
s
e
a
r
c
h
f
o
r
k
e
y
w
o
r
d
s
l
i
k
e
'1
9
th
-
c
e
n
t
u
r
y
n
o
v
e
l
s
,
'
o
r
'S
h
a
k
e
s
p
e
a
r
e
's
p
l
a
y
s.'
Li
t
e
r
a
t
u
r
e
sea
r
c
h
O
v
e
r
g
e
n
e
r
a
l
i
z
a
t
i
o
n
:
S
h
a
k
e
sp
e
a
r
e
’
s
w
o
r
k
s a
r
e
f
r
o
m
t
h
e
1
6
t
h
c
e
n
t
u
r
y
,
n
o
t
t
h
e
1
9
th
,
s
h
o
w
i
n
g
a
l
a
c
k
o
f
c
o
n
t
e
x
t
a
w
a
r
e
n
e
ss.
D
e
e
p
S
e
e
k
To
f
i
n
d
a
d
o
c
u
m
e
n
t
r
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l
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t
e
d
t
o
1
9
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-
c
e
n
t
u
r
y
l
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t
e
r
a
t
u
r
e
,
se
a
r
c
h
f
o
r
k
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y
w
o
r
d
s
l
i
k
e
'
1
9
th
c
e
n
t
u
r
y
n
o
v
e
l
s
,
'
o
r
'S
h
a
k
e
s
p
e
a
r
e
's
p
l
a
y
s.'
Li
t
e
r
a
t
u
r
e
sea
r
c
h
O
v
e
r
g
e
n
e
r
a
l
i
z
a
t
i
o
n
:
S
h
a
k
e
sp
e
a
r
e
’
s
w
o
r
k
s a
r
e
f
r
o
m
t
h
e
1
6
t
h
c
e
n
t
u
r
y
,
n
o
t
t
h
e
1
9
th
,
s
h
o
w
i
n
g
a
l
a
c
k
o
f
c
o
n
t
e
x
t
a
w
a
r
e
n
e
ss.
4
.
1
.
L
a
c
k
o
f
do
ma
in ex
pert
is
e
L
L
Ms a
r
e
tr
ain
ed
o
n
a
v
ar
iety
o
f
tex
t,
all
o
f
wh
ich
ar
e
d
er
iv
ed
f
r
o
m
th
e
in
ter
n
et,
an
d
th
e
r
ef
o
r
e,
co
v
er
m
an
y
to
p
ics.
W
h
ile
th
is
en
ab
les
th
em
to
g
en
er
ate
tex
t
o
n
a
wid
e
ar
r
ay
o
f
s
u
b
jects,
it
al
s
o
m
ea
n
s
th
ey
lack
in
-
d
ep
th
e
x
p
e
r
tis
e
in
an
y
s
p
ec
if
ic
d
o
m
ain
[
3
2
]
.
As
a
r
esu
lt,
wh
en
f
ac
ed
with
d
o
m
ain
-
s
p
ec
if
ic
q
u
er
ies
o
r
task
s
,
L
L
Ms
m
ay
p
r
o
d
u
ce
i
n
ac
cu
r
ate
o
r
ir
r
elev
a
n
t
r
esp
o
n
s
es.
I
n
o
th
e
r
wo
r
d
s
,
t
h
e
L
L
M
m
o
d
el
is
u
n
a
b
le
to
ac
cu
r
ately
u
n
d
e
r
s
tan
d
,
in
ter
p
r
et,
o
r
g
en
er
ate
tex
t
r
elate
d
to
s
p
ec
ialized
f
ield
s
.
T
h
is
le
ad
s
to
in
co
r
r
ec
t
o
r
o
v
er
s
im
p
lifi
ed
r
esp
o
n
s
es
wh
e
n
d
ea
lin
g
with
c
o
m
p
lex
,
tech
n
ical
to
p
ics.
Fo
r
ex
am
p
le,
in
m
ed
ical
d
iag
n
o
s
is
,
g
iv
en
th
e
q
u
er
y
"Wh
at
ar
e
th
e
d
if
f
er
en
tial
d
ia
g
n
o
s
es
f
o
r
a
p
atien
t
p
r
esen
tin
g
with
jau
n
d
ice,
elev
ated
liv
er
en
zy
m
es,
an
d
d
a
r
k
u
r
in
e?
"
T
h
e
L
L
M
r
esp
o
n
d
s
with
th
e
an
s
wer
"T
h
e
d
if
f
er
e
n
tial
d
iag
n
o
s
es
f
o
r
jau
n
d
ice
c
o
u
ld
in
clu
d
e
liv
er
d
is
ea
s
e,
g
allb
lad
d
er
p
r
o
b
lem
s
,
o
r
m
ay
b
e
s
o
m
e
k
in
d
o
f
in
f
ec
tio
n
.
Yo
u
s
h
o
u
ld
s
ee
a
d
o
cto
r
f
o
r
a
p
r
o
p
er
d
iag
n
o
s
is
.
"
T
h
e
r
esp
o
n
s
e
is
v
er
y
g
en
er
al
(
lack
s
s
p
ec
if
icity
)
,
d
o
es
n
o
t
m
en
tio
n
d
i
f
f
er
en
tia
l
d
iag
n
o
s
es
(
o
m
is
s
io
n
o
f
k
ey
d
iag
n
o
s
es),
an
d
f
ails
to
ex
p
lain
wh
y
th
e
s
p
ec
if
ic
s
y
m
p
to
m
s
p
o
in
t
to
war
d
s
th
ese
co
n
d
itio
n
s
(
n
o
d
etailed
u
n
d
e
r
s
tan
d
in
g
)
.
4
.
2
.
Understa
nd
ing
s
pecia
liz
ed
t
er
m
ino
lo
g
y
Ma
n
y
d
o
m
ai
n
s
h
av
e
s
p
ec
ializ
ed
ter
m
in
o
lo
g
y
o
r
jar
g
o
n
th
at
m
ay
n
o
t
b
e
co
m
m
o
n
ly
u
s
ed
in
ev
er
y
d
a
y
lan
g
u
ag
e.
L
L
Ms
m
ay
n
o
t
u
n
d
er
s
tan
d
o
r
co
r
r
ec
tly
u
s
e
s
u
ch
t
er
m
in
o
lo
g
y
with
o
u
t
s
p
ec
if
ic
tr
ain
in
g
o
r
e
x
p
o
s
u
r
e
to
d
o
m
ain
-
s
p
ec
i
f
ic
co
r
p
o
r
a.
T
h
is
lead
s
to
m
is
u
n
d
er
s
tan
d
in
g
s
o
r
m
is
in
ter
p
r
etatio
n
s
o
f
in
p
u
t
tex
t,
an
d
L
L
Ms
th
en
p
r
o
d
u
ce
n
o
n
s
en
s
ical
o
r
in
co
r
r
ec
t
o
u
t
p
u
ts
.
T
h
is
is
p
ar
ti
cu
lar
ly
ch
allen
g
in
g
in
f
ield
s
l
ik
e
m
ed
icin
e,
law,
en
g
in
ee
r
in
g
,
an
d
f
in
a
n
ce
,
wh
e
r
e
p
r
ec
is
e
lan
g
u
ag
e
is
cr
u
cial.
Fo
r
ex
am
p
le,
ass
u
m
e
th
at
th
e
u
s
er
q
u
er
y
is
“Can
y
o
u
ex
p
lain
th
e
im
p
licatio
n
s
o
f
th
e
d
o
ct
r
in
e
o
f
s
t
ar
e
d
ec
is
i
s
in
co
m
m
o
n
law
s
y
s
tem
s
?”
T
h
e
L
L
M
r
esp
o
n
d
s
with
“
Star
e
d
ec
is
is
is
a
leg
al
p
r
in
cip
le
th
at
co
u
r
ts
s
h
o
u
l
d
f
o
llo
w
p
r
e
v
io
u
s
d
ec
is
io
n
s
in
s
im
ilar
ca
s
es.
T
h
is
h
elp
s
en
s
u
r
e
co
n
s
is
ten
cy
an
d
p
r
ed
ictab
ilit
y
in
th
e
law.
”
T
h
e
r
esp
o
n
s
e
p
r
o
v
i
d
es
a
b
asic
d
ef
in
it
io
n
b
u
t
lack
s
d
ep
th
in
ex
p
lain
in
g
th
e
b
r
o
ad
e
r
im
p
licatio
n
s
an
d
a
p
p
licatio
n
s
o
f
th
e
d
o
ctr
i
n
e
o
f
s
tar
e
d
ec
is
is
.
Als
o
,
it
d
o
es
n
o
t
co
v
er
th
e
d
etails
s
u
ch
as
th
e
d
is
tin
ctio
n
b
etwe
en
b
in
d
in
g
an
d
p
er
s
u
asiv
e
p
r
ec
ed
en
ts
,
o
r
h
o
w
th
is
d
o
ctr
in
e
af
f
ec
ts
lo
wer
v
er
s
u
s
h
ig
h
er
co
u
r
ts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
4
,
Au
g
u
s
t 2
0
2
5
:
2
5
6
8
-
2
5
7
8
2572
4
.
3
.
Co
nte
x
t
ua
l und
er
s
t
a
nd
i
ng
E
f
f
ec
tiv
e
co
m
m
u
n
icatio
n
o
f
t
en
r
elies
o
n
u
n
d
er
s
tan
d
in
g
th
e
co
n
tex
t
in
w
h
ich
in
f
o
r
m
atio
n
is
ex
ch
an
g
e
d
.
L
L
Ms
m
a
y
n
o
t
g
r
asp
th
e
s
p
ec
if
ic
co
n
te
x
t
o
f
d
o
m
ain
-
s
p
ec
if
ic
co
n
v
er
s
atio
n
s
,
lead
in
g
t
o
m
is
in
ter
p
r
etatio
n
s
o
r
in
ap
p
r
o
p
r
iate
r
esp
o
n
s
es.
Fo
r
ex
am
p
le,
th
ey
m
ay
f
ail
to
r
ec
o
g
n
ize
s
u
b
tle
cu
es
o
r
r
ef
er
en
ce
s
th
at
ar
e
cr
u
cial
f
o
r
u
n
d
er
s
tan
d
in
g
th
e
m
ea
n
in
g
o
f
a
tex
t
in
a
p
ar
ticu
lar
d
o
m
ain
th
at
ap
p
r
o
p
r
iately
co
n
s
id
er
s
th
e
b
r
o
ad
er
c
o
n
tex
t
o
f
a
co
n
v
er
s
at
io
n
o
r
d
o
c
u
m
en
t
.
T
h
is
ca
n
lead
to
m
is
u
n
d
er
s
ta
n
d
in
g
s
o
r
ir
r
elev
an
t
r
esp
o
n
s
es.
Fo
r
e
x
am
p
le,
in
t
h
e
d
o
m
ai
n
o
f
cu
s
to
m
e
r
s
u
p
p
o
r
t
in
ter
ac
tio
n
,
g
iv
e
n
a
cu
s
to
m
er
q
u
er
y
th
at
r
ea
d
as
“Hi,
I
o
r
d
e
r
ed
a
lap
to
p
last
wee
k
an
d
it
ar
r
i
v
ed
y
ester
d
a
y
,
b
u
t
it'
s
n
o
t
tu
r
n
in
g
o
n
.
W
h
at
s
h
o
u
ld
I
d
o
?
”
T
h
e
L
L
M
r
esp
o
n
d
s
with
th
e
s
tatem
en
t
“
T
h
an
k
y
o
u
f
o
r
y
o
u
r
p
u
r
ch
ase!
T
o
p
lace
a
n
ew
o
r
d
er
,
p
lease
v
is
it
o
u
r
web
s
ite
an
d
b
r
o
wse th
r
o
u
g
h
o
u
r
wid
e
s
elec
tio
n
o
f
lap
t
o
p
s
.
”
T
h
is
r
esp
o
n
s
e
d
o
es n
o
t a
d
d
r
es
s
th
e
u
s
er
'
s
p
r
o
b
lem
o
f
th
e
n
o
n
-
f
u
n
ctio
n
a
l
la
p
to
p
.
I
n
s
tead
,
it
p
r
o
v
id
es
in
f
o
r
m
atio
n
o
n
h
o
w
to
p
lace
a
n
ew
o
r
d
er
,
w
h
ich
is
n
o
t
h
elp
f
u
l
in
th
is
co
n
tex
t
.
T
h
e
r
e
s
p
o
n
s
e
also
d
o
es
n
o
t
o
f
f
e
r
an
y
tr
o
u
b
lesh
o
o
tin
g
s
tep
s
,
r
etu
r
n
p
o
licy
in
f
o
r
m
atio
n
,
o
r
cu
s
to
m
er
s
u
p
p
o
r
t c
o
n
tact
d
etails,
wh
ich
ar
e
r
elev
an
t to
th
e
u
s
er
'
s
i
s
s
u
e.
4
.
4
.
Da
t
a
bia
s
T
h
e
d
ata
u
s
ed
to
t
r
ain
L
L
Ms
m
ay
n
o
t
ad
eq
u
ately
r
e
p
r
esen
t
all
d
o
m
ain
s
eq
u
ally
.
C
er
tain
d
o
m
ain
s
o
r
to
p
ics
m
ay
b
e
u
n
d
er
r
ep
r
esen
ted
o
r
m
is
r
ep
r
esen
ted
in
th
e
tr
ain
in
g
d
ata,
lead
in
g
to
b
ia
s
es
in
th
e
m
o
d
el's
u
n
d
er
s
tan
d
i
n
g
o
f
th
o
s
e
d
o
m
ai
n
s
.
T
h
is
ca
n
r
esu
lt
i
n
s
k
ewe
d
o
r
in
ac
c
u
r
ate
o
u
tp
u
ts
wh
en
g
e
n
er
atin
g
tex
t
r
elate
d
to
th
o
s
e
d
o
m
ain
s
.
A
s
im
p
le
e
x
am
p
le
f
o
r
d
ata
b
ias
is
th
e
d
o
m
ain
o
f
j
o
b
ap
p
licatio
n
.
W
h
en
p
r
o
m
p
ted
f
o
r
a
tem
p
late
f
o
r
a
jo
b
ap
p
licatio
n
,
th
e
L
L
M
ass
u
m
es
th
at
th
e
ap
p
lica
n
t
is
a
m
ale
an
d
r
etu
r
n
s
a
d
ef
au
lt
m
ale
n
am
e
an
d
g
e
n
d
er
-
s
p
ec
if
ic
w
o
r
d
s
s
u
c
h
as ‘
h
e”
.
4
.
5
.
T
ra
ns
f
er
lea
rning
lim
it
a
t
io
ns
T
r
an
s
f
er
lear
n
in
g
lev
er
a
g
es
k
n
o
wled
g
e
g
ai
n
ed
f
r
o
m
p
r
e
-
tr
a
in
in
g
o
n
a
lar
g
e,
d
iv
er
s
e
co
r
p
u
s
o
f
tex
t
d
ata
to
en
h
an
ce
p
er
f
o
r
m
an
ce
o
n
s
p
ec
if
ic
d
o
wn
s
tr
ea
m
task
s
o
r
d
o
m
ain
s
.
T
r
an
s
f
er
lea
r
n
in
g
allo
ws
L
L
Ms
to
ad
ap
t
to
s
p
ec
if
ic
task
s
o
r
d
o
m
ain
s
th
r
o
u
g
h
f
i
n
e
-
tu
n
i
n
g
.
H
o
wev
er
,
it
d
o
es
n
o
t
f
u
lly
a
d
d
r
e
s
s
th
e
ch
allen
g
es
o
f
d
o
m
ain
-
s
p
ec
if
ic
k
n
o
wled
g
e
a
n
d
co
n
tex
t.
Fo
r
ex
a
m
p
le,
as
s
u
m
e
L
L
M
is
g
iv
e
n
th
e
p
r
o
b
lem
o
f
d
o
c
u
m
en
t
an
aly
s
is
with
th
e
s
o
u
r
ce
d
o
m
ain
b
ein
g
“g
e
n
er
al
lan
g
u
a
g
e
m
o
d
elin
g
an
d
u
n
d
er
s
tan
d
in
g
o
f
ev
er
y
d
a
y
E
n
g
lis
h
”,
an
d
th
e
tar
g
et
d
o
m
ain
b
ein
g
“
an
aly
s
is
an
d
s
u
m
m
ar
izatio
n
o
f
leg
al
d
o
cu
m
en
ts
”.
Fu
r
t
h
er
,
a
s
s
u
m
e
th
at
th
e
u
s
er
r
eq
u
ests
f
o
r
s
u
m
m
ar
izatio
n
o
f
an
ex
ce
r
p
t
o
f
a
leg
al
d
o
cu
m
e
n
t
“
T
h
e
p
ar
ty
o
f
th
e
f
ir
s
t
p
ar
t
ag
r
ee
s
to
in
d
em
n
i
f
y
an
d
h
o
ld
h
ar
m
less
th
e
p
ar
ty
o
f
th
e
s
ec
o
n
d
p
ar
t
a
g
ain
s
t
all
liab
ilit
ies,
lo
s
s
es,
d
am
ag
es,
an
d
ex
p
e
n
s
es,
in
clu
d
in
g
atto
r
n
ey
'
s
f
ee
s
,
wh
i
ch
m
ay
ar
is
e
o
r
r
esu
lt
f
r
o
m
a
n
y
b
r
ea
ch
o
f
th
is
Ag
r
ee
m
en
t
o
r
f
r
o
m
th
e
ac
ts
o
r
o
m
is
s
io
n
s
o
f
th
e
p
ar
ty
o
f
th
e
f
ir
s
t
p
ar
t,
its
ag
en
ts
,
o
r
em
p
lo
y
ee
s
.
”
T
h
e
L
L
M
r
esp
o
n
d
s
as
“
T
h
e
f
ir
s
t
p
ar
ty
will
p
r
o
tect
th
e
s
ec
o
n
d
p
ar
ty
f
r
o
m
an
y
p
r
o
b
lem
s
th
at
a
r
is
e
”
wh
ich
is
o
v
er
-
s
im
p
lifie
d
,
c
o
m
p
letely
n
o
n
-
leg
al
i
n
n
u
an
ce
,
a
n
d
m
is
in
ter
p
r
eted
.
4
.
6
.
H
a
llu
cina
t
io
ns
Hallu
cin
atio
n
o
cc
u
r
s
wh
en
an
L
L
M
g
en
er
ates
in
f
o
r
m
a
tio
n
th
at
is
in
co
r
r
ec
t,
f
a
b
r
i
ca
ted
,
o
r
in
co
n
s
is
ten
t
with
r
ea
lity
[
3
3
]
,
o
f
ten
d
u
e
to
th
e
m
o
d
el’
s
in
ab
ilit
y
to
p
r
o
p
er
ly
h
a
n
d
le
s
p
ec
ialized
k
n
o
wled
g
e
o
r
co
n
tex
t.
T
h
is
c
an
h
ap
p
en
b
e
ca
u
s
e
th
e
m
o
d
el
h
as
g
en
er
al
k
n
o
wled
g
e
b
u
t
lac
k
s
a
d
ee
p
u
n
d
er
s
tan
d
in
g
o
f
s
p
ec
if
ic
leg
al
lan
g
u
ag
e,
p
r
e
ce
d
en
ts
,
o
r
co
n
tex
tu
al
f
ac
to
r
s
,
lead
in
g
to
th
e
g
en
er
atio
n
o
f
m
is
lead
in
g
o
r
co
m
p
letely
f
alse
in
f
o
r
m
atio
n
.
Fo
r
in
s
tan
ce
,
if
a
u
s
er
ask
s
a
n
L
L
M
-
"
W
h
o
p
lay
ed
th
e
lead
r
o
le
in
th
e
1
9
9
5
f
ilm
T
h
e
Ph
an
to
m
Ho
r
izo
n
?"
th
e
m
o
d
el
m
ig
h
t
in
v
en
t
an
ac
t
o
r
an
d
p
r
o
v
id
e
a
d
etailed
b
ac
k
s
to
r
y
,
ev
en
th
o
u
g
h
n
o
s
u
ch
m
o
v
ie
o
r
ac
to
r
e
x
is
ts
.
5.
SO
L
U
T
I
O
NS
So
m
e
o
f
th
e
d
o
m
ain
-
s
p
ec
if
ic
ch
allen
g
es
f
ac
ed
b
y
L
L
Ms
ar
e
d
is
cu
s
s
ed
in
s
ec
tio
n
4
.
Ma
n
y
o
f
th
ese
ch
allen
g
es
ca
n
b
e
m
itig
ated
b
y
f
in
e
-
tu
n
in
g
,
h
u
m
a
n
in
ter
v
en
tio
n
,
an
d
t
h
e
u
s
e
o
f
b
e
n
ch
m
ar
k
s
,
to
n
am
e
a
f
ew.
T
h
ese,
an
d
m
a
n
y
o
th
er
s
o
lu
tio
n
s
,
ar
e
p
r
esen
ted
in
th
is
s
ec
tio
n
.
5
.
1
.
So
lutio
ns
f
o
r
l
a
ck
o
f
do
m
a
in ex
pert
is
e
Sev
er
al
s
o
lu
tio
n
s
h
av
e
b
ee
n
p
r
o
p
o
s
ed
to
a
d
d
r
ess
th
e
lack
o
f
d
o
m
ain
ex
p
er
tis
e
in
L
L
Ms
.
Fin
e
-
tu
n
in
g
L
L
Ms o
n
d
o
m
ain
-
s
p
ec
if
ic
d
at
asets
ca
n
h
elp
th
em
ad
ap
t to
th
e
v
o
ca
b
u
la
r
y
,
s
ty
le,
an
d
in
tr
i
ca
cies o
f
a
p
ar
ticu
lar
d
o
m
ain
[
3
4
]
,
[
3
5
]
.
B
y
ex
p
o
s
i
n
g
th
e
m
o
d
el
to
d
o
m
ai
n
-
s
p
ec
if
ic
ex
am
p
les
d
u
r
in
g
f
in
e
-
tu
n
i
n
g
,
it
ca
n
lear
n
to
g
en
er
ate
m
o
r
e
ac
cu
r
ate
a
n
d
co
n
tex
tu
ally
r
ele
v
an
t
tex
t
f
o
r
th
at
d
o
m
ain
.
I
n
s
tead
o
f
s
tar
tin
g
f
r
o
m
g
en
er
ic
p
r
e
-
tr
ain
in
g
,
L
L
Ms
ca
n
b
e
p
r
e
-
tr
ain
ed
o
n
d
o
m
ain
-
s
p
ec
if
ic
co
r
p
o
r
a
o
r
with
d
o
m
ain
-
s
p
ec
i
f
ic
o
b
jectiv
es.
T
h
is
allo
ws
th
e
m
o
d
el
to
ca
p
tu
r
e
d
o
m
ain
-
s
p
ec
if
ic
k
n
o
wled
g
e
a
n
d
p
atter
n
s
d
u
r
in
g
p
r
e
-
t
r
ain
in
g
,
lead
in
g
to
b
etter
p
er
f
o
r
m
an
ce
in
th
at
d
o
m
ain
.
Kn
o
wled
g
e
d
is
till
atio
n
tech
n
iq
u
es
ar
e
an
o
th
e
r
s
o
lu
tio
n
f
o
r
lack
o
f
d
o
m
ai
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
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8
9
3
8
Do
ma
in
-
s
p
ec
ific k
n
o
w
led
g
e
a
n
d
co
n
text
i
n
la
r
g
e
la
n
g
u
a
g
e
mo
d
els
…
(
K
ir
a
n
Ma
ye
e
A
d
a
v
a
la
)
2573
ex
p
er
tis
e,
wh
ich
in
v
o
lv
e
tr
an
s
f
er
r
in
g
k
n
o
wled
g
e
f
r
o
m
d
o
m
a
in
ex
p
er
ts
to
th
e
L
L
Ms.
T
h
is
c
an
b
e
d
o
n
e
th
r
o
u
g
h
s
u
p
er
v
is
ed
lea
r
n
in
g
,
wh
e
r
e
ex
p
er
ts
p
r
o
v
i
d
e
an
n
o
tatio
n
s
o
r
c
o
r
r
ec
tio
n
s
to
t
h
e
m
o
d
el'
s
o
u
tp
u
ts
[
3
6
]
,
o
r
t
h
r
o
u
g
h
in
ter
ac
tiv
e
m
eth
o
d
s
wh
er
e
ex
p
er
ts
g
u
id
e
th
e
m
o
d
el'
s
b
eh
av
io
r
in
r
ea
l
-
tim
e.
Pro
m
p
t
en
g
i
n
ee
r
in
g
in
v
o
l
v
es
d
esig
n
in
g
tailo
r
e
d
p
r
o
m
p
ts
o
r
in
p
u
t
f
o
r
m
ats
th
at
g
u
id
e
th
e
L
L
Ms
to
p
r
o
d
u
ce
d
o
m
ain
-
s
p
ec
if
ic
o
u
t
p
u
ts
.
B
y
p
r
o
v
id
in
g
c
o
n
tex
t
o
r
co
n
s
t
r
ain
ts
r
elev
an
t
t
o
th
e
d
o
m
ain
,
p
r
o
m
p
t
en
g
in
ee
r
in
g
ca
n
h
elp
s
teer
th
e
m
o
d
el
to
war
d
s
g
en
e
r
atin
g
m
o
r
e
ac
cu
r
ate
an
d
r
elev
an
t
te
x
t.
Data
au
g
m
en
tatio
n
tech
n
iq
u
es
ca
n
b
e
u
s
ed
to
i
n
cr
ea
s
e
th
e
d
iv
er
s
ity
an
d
c
o
v
er
ag
e
o
f
d
o
m
ain
-
s
p
ec
if
ic
tr
ain
in
g
d
ata
[
3
7
]
.
E
ith
er
s
y
n
th
esizin
g
ad
d
itio
n
al
tr
ain
in
g
e
x
am
p
les o
r
a
u
g
m
e
n
tin
g
e
x
is
tin
g
o
n
es
ca
n
en
ab
le
L
L
Ms
b
etter
ca
p
t
u
r
e
th
e
ch
ar
ac
ter
is
tics
o
f
a
p
ar
tic
u
lar
d
o
m
ai
n
.
E
n
s
em
b
le
m
eth
o
d
s
co
m
b
in
e
m
u
ltip
le
L
L
Ms
tr
ain
ed
o
n
d
if
f
er
e
n
t
d
o
m
ain
s
o
r
u
s
in
g
d
if
f
e
r
en
t
p
r
e
-
tr
ain
in
g
s
tr
ateg
ies.
B
y
ag
g
r
eg
at
in
g
th
e
o
u
tp
u
ts
o
f
d
iv
er
s
e
m
o
d
els,
en
s
em
b
le
ap
p
r
o
ac
h
es
ca
n
im
p
r
o
v
e
r
o
b
u
s
tn
ess
an
d
p
er
f
o
r
m
an
ce
ac
r
o
s
s
a
r
an
g
e
o
f
d
o
m
ain
s
.
Hy
b
r
i
d
m
o
d
els
co
m
b
i
n
e
th
e
s
tr
en
g
th
s
o
f
L
L
Ms
with
d
o
m
ain
-
s
p
ec
if
ic
m
o
d
els
o
r
k
n
o
wled
g
e
b
ases
.
B
y
in
teg
r
ati
n
g
d
o
m
ain
-
s
p
ec
if
ic
m
o
d
u
les
o
r
e
x
ter
n
al
k
n
o
wled
g
e
s
o
u
r
ce
s
,
h
y
b
r
id
m
o
d
els
ca
n
lev
er
ag
e
b
o
th
th
e
g
en
e
r
aliza
tio
n
ca
p
ab
ilit
ies
o
f
L
L
Ms
an
d
th
e
s
p
ec
ialized
ex
p
er
tis
e
o
f
d
o
m
ain
-
s
p
ec
if
ic
m
o
d
els.
Z
er
o
-
s
h
o
t
an
d
f
ew
-
s
h
o
t
l
ea
r
n
in
g
tech
n
i
q
u
es
en
ab
le
L
L
Ms
t
o
p
er
f
o
r
m
task
s
in
n
ew
d
o
m
ain
s
with
lim
ited
o
r
n
o
tr
ain
in
g
d
a
ta.
B
y
lev
e
r
ag
in
g
tr
an
s
f
er
lear
n
in
g
an
d
m
eta
-
lea
r
n
in
g
ap
p
r
o
ac
h
es,
L
L
Ms
ca
n
g
en
er
alize
ac
r
o
s
s
d
o
m
ai
n
s
an
d
ad
a
p
t
to
n
ew
task
s
m
o
r
e
ef
f
ec
tiv
el
y
.
5
.
2
.
So
lutio
ns
f
o
r
un
dersta
nd
ing
s
pecia
lize
d t
er
m
ino
lo
g
y
Pre
-
tr
ain
in
g
L
L
Ms
o
n
d
o
m
ai
n
-
s
p
ec
if
ic
co
r
p
o
r
a
en
s
u
r
es
th
at
th
e
m
o
d
el
is
ex
p
o
s
ed
to
th
e
te
r
m
in
o
lo
g
y
an
d
c
o
n
tex
t o
f
a
p
a
r
ticu
lar
f
ield
[
3
8
]
.
Fo
r
i
n
s
tan
ce
,
tr
ain
i
n
g
o
n
m
ed
ical
liter
atu
r
e
f
o
r
h
ea
lth
ca
r
e
ap
p
licatio
n
s
is
a
g
o
o
d
e
x
am
p
le
f
o
r
p
r
e
-
t
r
ain
in
g
o
n
d
o
m
ain
-
s
p
ec
if
ic
co
r
p
o
r
a
[
3
9
]
.
C
o
n
tin
u
in
g
th
e
p
r
e
-
tr
ain
in
g
p
h
ase
with
ad
d
itio
n
al
d
o
m
ain
-
s
p
ec
if
ic
d
a
ta
to
en
h
an
ce
th
e
m
o
d
el'
s
u
n
d
er
s
tan
d
in
g
o
f
s
p
ec
ialized
te
r
m
s
ca
n
also
h
elp
u
n
d
er
s
tan
d
s
p
ec
ialized
ter
m
in
o
lo
g
y
[
4
0
]
.
Fin
e
-
tu
n
in
g
L
L
M
s
o
n
d
atasets
th
at
ar
e
s
p
ec
if
i
ca
lly
cu
r
ated
f
o
r
a
p
ar
ticu
lar
d
o
m
ain
o
r
task
ca
n
s
ig
n
if
ican
tly
im
p
r
o
v
e
th
eir
p
er
f
o
r
m
a
n
ce
in
u
n
d
er
s
tan
d
i
n
g
a
n
d
g
en
e
r
atin
g
r
elev
an
t te
r
m
in
o
l
o
g
y
.
Usi
n
g
la
b
eled
d
ata
(
s
u
p
er
v
is
ed
lear
n
in
g
)
wh
er
e
th
e
co
r
r
ec
t u
s
ag
e
o
f
s
p
ec
ialized
ter
m
s
is
ex
p
licitly
m
ar
k
ed
,
h
elp
s
th
e
m
o
d
el
lear
n
th
e
co
r
r
ec
t
co
n
te
x
t
an
d
m
ea
n
in
g
.
I
n
teg
r
atin
g
s
tr
u
ctu
r
ed
k
n
o
wled
g
e
b
ases
(
Kn
o
wled
g
e
g
r
ap
h
in
t
eg
r
atio
n
)
lik
e
m
ed
ical
o
n
to
l
o
g
ies
o
r
leg
al
d
atab
ases
ca
n
h
elp
L
L
Ms
ac
ce
s
s
p
r
ec
is
e
d
ef
in
itio
n
s
an
d
r
elat
io
n
s
h
ip
s
b
etwe
en
s
p
ec
ialized
ter
m
s
.
L
in
k
in
g
en
titi
es
in
th
e
tex
t
to
th
eir
co
r
r
esp
o
n
d
in
g
e
n
tr
ies
in
a
k
n
o
wled
g
e
b
ase
en
s
u
r
es
th
e
m
o
d
el
u
n
d
er
s
tan
d
s
an
d
u
s
es
th
e
co
r
r
ec
t
ter
m
in
o
l
o
g
y
.
Dir
ec
tly
in
teg
r
a
tin
g
s
p
ec
iali
ze
d
g
lo
s
s
ar
ies
an
d
d
ictio
n
a
r
ies
in
to
th
e
m
o
d
el’
s
tr
ain
i
n
g
d
ata
h
elp
s
in
u
n
d
er
s
tan
d
i
n
g
a
n
d
c
o
r
r
ec
tly
u
s
in
g
d
o
m
ain
-
s
p
ec
if
ic
ter
m
s
.
Allo
win
g
th
e
m
o
d
el
to
d
y
n
am
ically
ac
ce
s
s
an
d
q
u
er
y
s
p
ec
ialized
g
lo
s
s
ar
ies d
u
r
in
g
in
f
er
en
ce
im
p
r
o
v
es a
cc
u
r
ac
y
in
r
e
al
-
tim
e
.
E
x
p
er
t
a
n
n
o
tatio
n
s
i
n
v
o
lv
i
n
g
d
o
m
ain
e
x
p
er
ts
an
d
p
o
s
s
ib
ly
k
n
o
wled
g
e
in
jectio
n
[
4
1
]
to
a
n
n
o
tate
an
d
r
ev
iew
m
o
d
el
o
u
tp
u
ts
en
s
u
r
e
s
th
e
co
r
r
ec
t
u
s
ag
e
o
f
s
p
ec
i
alize
d
ter
m
in
o
lo
g
y
.
Sy
s
tem
s
wh
er
e
ex
p
e
r
ts
ca
n
in
ter
ac
tiv
ely
co
r
r
ec
t
an
d
p
r
o
v
id
e
f
ee
d
b
ac
k
to
t
h
e
m
o
d
el
u
s
in
g
k
n
o
wled
g
e
g
r
ap
h
s
[
4
2
]
en
ab
les
th
e
m
o
d
el
t
o
lear
n
f
r
o
m
t
h
ese
co
r
r
ec
tio
n
s
.
C
o
n
tin
u
o
u
s
ly
u
p
d
atin
g
th
e
m
o
d
el
with
n
ew
d
ata
an
d
ter
m
in
o
lo
g
y
as
th
e
d
o
m
ai
n
ev
o
lv
es,
e
n
s
u
r
es
th
at
it
s
tay
s
c
u
r
r
en
t
with
th
e
latest ter
m
s
an
d
th
eir
m
ea
n
in
g
s
[
4
3
]
.
I
m
p
lem
en
tin
g
m
ec
h
an
is
m
s
f
o
r
t
h
e
m
o
d
el
to
lear
n
a
n
d
ad
a
p
t
to
n
ew
ter
m
in
o
lo
g
y
d
y
n
am
i
ca
lly
(
ad
a
p
tiv
e
lear
n
in
g
)
as
it
en
co
u
n
ter
s
th
em
in
n
ew
tex
ts
is
also
a
g
o
o
d
m
e
ch
an
is
m
f
o
r
u
n
d
er
s
tan
d
in
g
n
ew
ter
m
in
o
lo
g
y
.
Sp
ec
ialized
m
o
d
el
ar
c
h
itectu
r
es
s
u
ch
as
h
y
b
r
id
m
o
d
els,
wh
ich
co
m
b
i
n
e
g
e
n
er
al
L
L
Ms
with
s
m
aller
,
d
o
m
ain
-
s
p
ec
if
ic
m
o
d
els
th
at
ar
e
e
x
p
er
ts
in
u
n
d
e
r
s
tan
d
in
g
an
d
g
en
e
r
atin
g
s
p
ec
ialized
ter
m
in
o
lo
g
y
,
an
d
m
o
d
u
lar
ap
p
r
o
ac
h
es
wh
i
ch
u
s
e
a
m
o
d
u
lar
ar
ch
itectu
r
e
wh
er
e
d
if
f
er
e
n
t
c
o
m
p
o
n
en
ts
o
f
th
e
m
o
d
el
s
p
ec
i
alize
in
d
if
f
er
en
t
d
o
m
ain
s
an
d
ca
n
b
e
s
elec
tiv
ely
ac
tiv
ated
b
ased
o
n
th
e
task
ar
e
eq
u
ally
u
s
ef
u
l f
o
r
u
n
d
er
s
tan
d
i
n
g
s
p
ec
ialized
ter
m
in
o
lo
g
y
.
Mix
tu
r
e
o
f
e
x
p
er
ts
(
M
o
E
)
is
a
v
a
r
iatio
n
o
f
th
e
e
x
p
er
t
a
n
n
o
tatio
n
s
s
o
lu
tio
n
th
at
in
v
o
lv
es
u
s
in
g
m
u
ltip
le
s
p
ec
ialized
m
o
d
els
o
r
ex
p
er
ts
with
in
a
lar
g
er
m
o
d
el,
ea
ch
tr
ain
e
d
o
n
d
if
f
er
e
n
t
d
o
m
ain
s
o
r
task
s
.
W
h
en
a
u
s
er
q
u
er
ies
th
e
m
o
d
el,
it
ac
tiv
ates
o
n
ly
th
e
m
o
s
t
r
elev
an
t
ex
p
er
t(
s
)
f
o
r
th
at
p
ar
ticu
lar
d
o
m
ain
[
4
4
]
[
45]
,
en
a
b
lin
g
th
e
m
o
d
el
to
ac
ce
s
s
h
ig
h
ly
s
p
ec
ialized
k
n
o
wled
g
e
with
o
u
t
o
v
er
l
o
ad
in
g
th
e
s
y
s
tem
.
T
h
is
ap
p
r
o
ac
h
allo
ws
L
L
Ms
to
h
a
n
d
le
d
iv
er
s
e
d
o
m
ain
s
m
o
r
e
e
f
f
ec
tiv
ely
,
en
s
u
r
in
g
th
at
c
o
m
p
lex
task
s
—
s
u
ch
as
m
ed
ical
d
iag
n
o
s
is
o
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ca
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p
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m
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ap
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iate
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x
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Mo
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h
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alize
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o
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ically
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ig
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"e
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Dev
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ialized
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ai
n
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d
co
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ctin
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lar
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ates
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m
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h
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g
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ter
m
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o
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.
5
.
3
.
So
lut
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f
o
r
l
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ck
o
f
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nte
x
t
ua
l und
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a
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n
h
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p
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tech
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ial
tr
ain
in
g
m
o
d
els
u
s
in
g
h
ig
h
q
u
ality
d
ata
[
4
6
]
a
n
d
m
o
d
el
ar
ch
itectu
r
es
ca
n
r
ed
u
ce
lack
o
f
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ial
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ess
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r
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co
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tex
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Sp
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m
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ch
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in
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co
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c
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tex
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s
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els
with
en
h
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atten
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m
s
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th
at
ca
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ca
p
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e
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cr
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t sp
an
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)
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with
co
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tex
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al
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n
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er
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tan
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g
.
L
L
Ms
ca
n
b
e
f
in
e
-
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n
e
d
in
two
way
s
-
o
n
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atasets
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at
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h
asize
co
n
tex
tu
al
co
h
er
en
ce
an
d
c
o
n
tin
u
ity
[
4
7
]
,
s
u
ch
as
lo
n
g
-
f
o
r
m
co
n
v
er
s
atio
n
s
,
ch
ain
-
of
-
th
o
u
g
h
t
[
4
8
]
,
o
r
n
ar
r
ativ
e
te
x
ts
(
co
n
tex
t
-
r
ic
h
f
in
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tu
n
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g
)
,
a
n
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s
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g
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ialo
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d
atasets
wh
er
e
th
e
co
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tex
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cr
itical
f
o
r
m
ain
tain
in
g
th
e
f
lo
w
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d
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a
n
ce
o
f
th
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n
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er
s
atio
n
,
h
elp
in
g
m
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d
els
lear
n
t
h
e
in
tr
icac
ies
o
f
c
o
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tex
t
-
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p
en
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en
t
in
ter
ac
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n
s
(
co
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v
e
r
s
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al
f
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tu
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).
An
o
th
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s
o
lu
tio
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u
s
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em
o
r
y
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m
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s
u
ch
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ex
ter
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m
em
o
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y
m
ec
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is
m
s
wh
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d
ea
ls
with
in
teg
r
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ex
ter
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al
m
e
m
o
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y
co
m
p
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n
en
ts
to
allo
w
m
o
d
els
to
s
to
r
e
an
d
r
etr
iev
e
r
elev
a
n
t
co
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tex
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al
in
f
o
r
m
atio
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as
n
ee
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ed
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,
an
d
r
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R
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,
wh
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co
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b
in
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r
etr
iev
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m
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h
a
n
is
m
s
with
g
en
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[
4
9
]
,
wh
er
e
th
e
m
o
d
el
r
etr
iev
es
r
ele
v
an
t
d
o
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m
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to
aid
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en
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atin
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tex
tu
ally
ac
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ate
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s
es.
T
wo
h
ier
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r
ch
ical
m
o
d
els
ar
e
o
f
im
p
o
r
tan
ce
in
c
o
n
tex
t
u
n
d
er
s
tan
d
i
n
g
.
T
h
e
f
ir
s
t
is
th
e
im
p
lem
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tatio
n
o
f
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ie
r
ar
ch
ical
atten
tio
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m
ec
h
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is
m
s
th
at
ca
n
p
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s
co
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tex
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at
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ltip
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e.
g
.
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s
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ten
ce
,
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d
d
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t u
n
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r
s
tan
d
in
g
o
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lo
n
g
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ts
.
T
h
e
s
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d
is
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g
m
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els th
at
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p
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ate
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tex
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if
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g
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lar
ities
,
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s
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r
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d
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tex
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ase
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at
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ts
.
T
h
e
s
am
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ca
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also
b
e
im
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lem
en
ted
b
y
e
m
p
lo
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in
g
cr
o
s
s
-
atten
tio
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m
ec
h
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is
m
s
th
at
ca
n
d
y
n
am
ically
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ju
s
t
th
e
f
o
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s
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n
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t p
ar
ts
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f
th
e
co
n
tex
t d
u
r
in
g
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f
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.
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tex
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awa
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l
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s
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ctio
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th
at
p
e
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alize
in
co
h
e
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d
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tex
tu
ally
ir
r
elev
an
t o
u
tp
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ts
en
co
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r
a
g
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th
e
m
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d
el
to
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ate
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o
r
e
co
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tex
t
u
ally
ap
p
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o
p
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iate
r
esp
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s
es.
On
e
co
u
ld
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im
p
lem
en
t
s
eq
u
en
tial
tr
ain
in
g
o
b
jectiv
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s
th
at
ex
p
licitly
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o
c
u
s
o
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u
n
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s
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n
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a
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m
ai
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tain
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tex
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ac
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s
s
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ask
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ag
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o
d
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with
co
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tex
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win
d
o
ws.
A
p
r
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m
p
tin
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ewo
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s
L
L
M4
C
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ca
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e
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teg
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m
o
r
e
ef
f
icien
t
d
eter
m
in
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n
o
f
co
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tex
t
[
5
0
]
.
Hu
m
an
-
in
-
th
e
-
l
o
o
p
s
y
s
tem
s
s
u
ch
as
th
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e
th
at
all
o
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u
m
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s
er
s
to
p
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v
id
e
r
e
al
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tim
e
f
ee
d
b
ac
k
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n
t
h
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m
o
d
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l’
s
o
u
tp
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ts
,
h
elp
t
h
e
m
o
d
el
lear
n
to
b
etter
m
ain
tain
an
d
u
tili
ze
co
n
tex
t
in
f
u
tu
r
e
in
ter
ac
tio
n
s
.
Alo
n
g
with
th
i
s
,
lev
er
ag
in
g
ex
p
er
t
an
n
o
tatio
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s
h
elp
to
c
o
r
r
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t
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d
g
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h
e
m
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d
el
in
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n
d
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s
tan
d
in
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co
m
p
lex
co
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tex
ts
,
im
p
r
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v
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g
its
co
n
te
x
tu
al
co
m
p
r
eh
e
n
s
io
n
o
v
er
tim
e.
An
o
th
er
s
o
lu
tio
n
is
th
e
d
ev
e
lo
p
m
en
t
o
f
co
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tex
t
u
al
b
en
c
h
m
ar
k
s
s
p
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if
ically
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ed
t
o
test
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e
m
o
d
el’
s
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to
u
n
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tan
d
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e
n
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ate
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te
x
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tp
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ts
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ch
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g
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f
o
r
m
QA
d
atasets
o
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m
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ig
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m
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ce
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n
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r
s
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an
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tili
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tex
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ac
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o
s
s
d
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s
e
ap
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licatio
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s
.
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o
,
in
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ex
ter
n
al
wo
r
ld
k
n
o
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e.
g
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atab
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p
r
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T
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s
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n
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e
o
b
tain
ed
b
y
im
p
lem
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tin
g
m
ec
h
a
n
is
m
s
th
at
allo
w
th
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m
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d
el
to
d
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ically
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ate
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ld
k
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ased
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th
e
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x
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Fin
ally
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s
lo
w
th
in
k
in
g
,
a
c
o
n
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ep
t
u
s
ed
in
m
o
d
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lik
e
Op
en
AI
0
1
,
ca
n
b
e
im
p
lem
en
ted
t
o
g
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th
e
m
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m
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etter
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n
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ap
p
r
o
ac
h
im
p
r
o
v
es
ac
cu
r
ac
y
b
y
en
ab
li
n
g
th
e
m
o
d
el
to
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n
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id
er
a
d
d
itio
n
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o
f
i
n
f
o
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m
atio
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,
cr
o
s
s
-
r
ef
e
r
en
ce
d
etails,
an
d
r
ed
u
ce
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r
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r
s
,
esp
ec
ially
in
s
p
ec
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ea
s
th
at
r
eq
u
ir
e
a
d
ee
p
e
r
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n
d
e
r
s
tan
d
in
g
,
s
u
ch
as
law
o
r
s
cien
tific
r
esear
ch
.
5
.
4
.
So
lutio
ns
f
o
r
da
t
a
bia
s
C
u
r
ati
n
g
d
at
asets
th
at
a
r
e
m
o
r
e
b
ala
n
c
e
d
a
n
d
r
ep
r
ese
n
ta
ti
v
e
o
f
d
i
f
f
e
r
e
n
t
d
e
m
o
g
r
ap
h
i
cs,
c
u
l
tu
r
es
,
a
n
d
v
ie
wp
o
i
n
ts
h
el
p
s
r
e
d
u
ce
b
ias
es
i
n
t
r
ai
n
i
n
g
d
ata
.
S
im
ila
r
l
y
,
o
n
e
s
h
o
u
l
d
u
s
e
tec
h
n
i
q
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es
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g
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en
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n
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er
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p
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ta
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ts
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n
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r
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h
at
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in
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p
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t
ed
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n
t
h
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t
r
ai
n
i
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g
p
r
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ce
s
s
.
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m
p
l
em
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ti
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g
t
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al
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ase
d
p
at
te
r
n
s
t
h
at
n
ee
d
ad
d
r
ess
i
n
g
[
5
1
]
.
Ut
ili
zi
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g
a
d
v
e
r
s
a
r
ia
l t
r
ai
n
i
n
g
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et
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o
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s
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d
a
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m
o
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el
(
a
d
v
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s
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ai
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t
o
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et
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b
iase
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u
tc
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m
es
c
a
n
als
o
m
iti
g
ate
b
ias
[
5
2
]
,
[
5
3
]
.
A
p
p
l
y
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n
g
b
i
as
c
o
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r
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cti
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m
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t
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d
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e
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r
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b
ei
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p
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te
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E
x
p
e
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r
e
v
ie
w
i
n
v
o
l
v
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g
h
u
m
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x
p
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ts
(
R
L
HF
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d
is
c
u
s
s
e
d
i
n
s
e
cti
o
n
5
.
6
)
ca
n
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el
p
r
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v
iew
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d
c
o
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e
ct
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iase
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etr
ai
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a
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d
im
p
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o
v
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h
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m
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l.
Usi
n
g
d
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v
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e
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r
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p
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f
an
n
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to
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s
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ca
n
p
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in
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p
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g
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tr
ea
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en
t.
E
n
s
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r
in
g
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m
p
lian
ce
with
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licies
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d
r
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latio
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s
r
elate
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to
AI
f
air
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ess
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et
h
ics,
s
u
ch
as
g
en
er
al
d
ata
p
r
o
tectio
n
r
eg
u
latio
n
(
GDPR
)
o
r
th
e
AI
ac
t
p
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p
o
s
ed
b
y
t
h
e
E
u
r
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p
ea
n
Un
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n
b
y
estab
lis
h
in
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clea
r
p
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o
to
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ls
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o
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tr
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s
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d
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ilit
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u
d
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th
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ilit
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it a
n
d
ex
p
lain
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e
m
o
d
el’
s
d
ec
is
io
n
s
an
d
its
p
o
ten
tial b
iases
.
5
.
5
.
So
lutio
ns
f
o
r
t
ra
ns
f
er
lea
rning
lim
it
a
t
io
ns
Pre
-
tr
ain
in
g
L
L
Ms
o
n
d
o
m
ai
n
-
s
p
ec
if
ic
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atasets
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r
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v
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s
t
r
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f
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d
atio
n
i
n
th
e
r
ele
v
a
n
t
co
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tex
t
an
d
ter
m
in
o
lo
g
y
b
ef
o
r
e
f
in
e
-
tu
n
in
g
o
n
s
p
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if
ic
task
s
.
C
o
m
b
in
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g
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al
an
d
d
o
m
ain
-
s
p
ec
if
ic
p
r
e
-
tr
ai
n
in
g
[
5
4
]
p
h
ases
b
alan
ce
s
b
r
o
ad
lan
g
u
ag
e
u
n
d
e
r
s
tan
d
in
g
with
s
p
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ialized
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n
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wled
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e.
Utilizin
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f
ew
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s
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o
t
lear
n
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g
ap
p
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h
es
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e
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h
e
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is
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e
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v
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m
all
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n
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task
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s
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ec
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ata
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g
es
its
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r
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x
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g
k
n
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e
ef
f
ec
ti
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ely
.
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m
p
lem
e
n
tin
g
m
eta
-
lear
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g
alg
o
r
ith
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s
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elp
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tr
ain
th
e
m
o
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el
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q
u
ic
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ly
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t
to
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ew
task
s
with
m
in
im
al
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ata
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y
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n
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g
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o
w
t
o
lear
n
d
u
r
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g
th
e
tr
ain
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n
g
p
h
ase.
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o
n
tin
u
o
u
s
ly
u
p
d
atin
g
th
e
m
o
d
el
with
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ew
d
ata
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r
o
m
d
if
f
er
e
n
t
task
s
an
d
d
o
m
ain
s
k
ee
p
s
it
cu
r
r
e
n
t
a
n
d
v
e
r
s
atile.
Usi
n
g
tech
n
iq
u
es su
ch
as
elastic w
ei
g
h
t c
o
n
s
o
lid
atio
n
(
E
W
C
)
p
r
ev
en
ts
th
e
m
o
d
el
f
r
o
m
f
o
r
g
ettin
g
p
r
ev
io
u
s
ly
lear
n
e
d
task
s
wh
en
tr
ain
ed
o
n
n
ew
o
n
es (
ca
tast
r
o
p
h
ic
f
o
r
g
ettin
g
m
it
ig
atio
n
).
T
r
ain
in
g
m
o
d
els
to
d
ev
el
o
p
task
-
ag
n
o
s
tic
r
ep
r
esen
tatio
n
s
th
at
ca
p
tu
r
e
f
u
n
d
am
en
tal
asp
ec
ts
o
f
lan
g
u
ag
e
m
ak
es
th
em
m
o
r
e
a
d
ap
tab
le
to
a
v
ar
iety
o
f
task
s
.
Als
o
,
u
s
e
o
f
s
elf
-
s
u
p
er
v
is
ed
l
ea
r
n
in
g
tech
n
i
q
u
es
cr
ea
tes
r
o
b
u
s
t
r
ep
r
esen
tatio
n
s
th
at
r
eq
u
ir
e
m
in
im
al
ad
ju
s
tm
en
t
wh
en
tr
an
s
f
er
r
ed
t
o
n
ew
tas
k
s
.
T
r
ain
in
g
m
o
d
els
with
co
n
d
itio
n
al
in
p
u
t
s
th
at
s
p
ec
if
y
th
e
task
o
r
d
o
m
ain
en
ab
les
th
e
m
o
d
el
to
ad
ju
s
t
its
b
eh
av
io
r
b
ased
o
n
th
e
g
iv
e
n
co
n
te
x
t.
On
th
e
o
th
er
h
an
d
,
tr
ain
in
g
th
e
m
o
d
e
l
with
d
iv
er
s
e
an
d
co
n
te
x
t
-
r
ic
h
d
ata
im
p
r
o
v
es
its
ab
ilit
y
to
g
en
er
alize
ac
r
o
s
s
d
if
f
er
en
t
s
ce
n
ar
io
s
.
L
ea
r
n
in
g
ef
f
i
cien
cy
ca
n
b
e
f
u
r
th
er
im
p
r
o
v
e
d
b
y
im
p
lem
en
tin
g
ac
tiv
e
lear
n
in
g
s
tr
ateg
ies
wh
er
e
th
e
m
o
d
el
ca
n
q
u
er
y
f
o
r
th
e
m
o
s
t
in
f
o
r
m
ativ
e
d
ata.
B
y
in
co
r
p
o
r
atin
g
m
ec
h
an
is
m
s
f
o
r
h
u
m
an
f
ee
d
b
ac
k
to
c
o
r
r
ec
t
a
n
d
g
u
id
e
th
e
m
o
d
el’
s
lear
n
i
n
g
p
r
o
ce
s
s
,
o
n
e
ca
n
e
n
h
an
ce
its
ab
ilit
y
to
a
d
ap
t
to
n
ew
task
s
.
On
e
ca
n
also
d
e
v
elo
p
an
d
u
til
ize
b
en
ch
m
ar
k
s
s
p
ec
if
ically
d
esig
n
ed
to
test
th
e
m
o
d
el’
s
tr
an
s
f
er
lear
n
in
g
ca
p
a
b
ilit
ies
ac
r
o
s
s
v
ar
io
u
s
d
o
m
ain
s
an
d
task
s
.
R
eg
u
lar
ly
co
n
d
u
ct
in
g
ass
ess
m
en
ts
o
f
th
e
m
o
d
el’
s
p
er
f
o
r
m
a
n
ce
o
n
d
if
f
er
en
t
task
s
an
d
d
o
m
ain
s
ca
n
h
elp
in
id
e
n
tify
in
g
a
n
d
ad
d
r
ess
in
g
an
y
tr
an
s
f
er
lear
n
in
g
lim
itatio
n
s
.
T
h
e
u
s
e
o
f
k
n
o
wled
g
e
d
is
till
atio
n
tech
n
iq
u
es wh
er
e
a
lar
g
e,
well
-
tr
ain
ed
m
o
d
el
(
teac
h
er
)
tr
an
s
f
er
s
it
s
k
n
o
wled
g
e
to
a
s
m
aller
,
task
-
sp
ec
if
ic
m
o
d
el
(
s
tu
d
en
t)
ca
n
h
e
lp
th
e
s
tu
d
en
t m
o
d
el
to
lear
n
ef
f
ec
tiv
ely
f
r
o
m
th
e
teac
h
er
’
s
k
n
o
wled
g
e.
Als
o
,
u
tili
zin
g
s
o
f
t
tar
g
ets
(
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
s
)
r
ath
er
th
an
h
ar
d
tar
g
ets
(
class
lab
els),
ca
n
g
u
id
e
th
e
s
tu
d
en
t
m
o
d
el,
th
er
e
b
y
im
p
r
o
v
in
g
i
ts
ab
ilit
y
to
g
en
er
alize
.
I
n
ad
d
itio
n
,
in
t
r
o
d
u
cin
g
au
x
iliar
y
task
s
d
u
r
i
n
g
f
in
e
-
tu
n
in
g
,
s
u
ch
as
s
en
ten
ce
co
m
p
l
etio
n
,
m
ask
e
d
lan
g
u
ag
e
m
o
d
e
lin
g
,
o
r
p
ar
ap
h
r
ase
d
etec
tio
n
,
en
h
a
n
ce
s
th
e
m
o
d
el’
s
r
o
b
u
s
tn
ess
an
d
ad
a
p
tab
ilit
y
to
n
ew
task
s
.
C
o
n
d
u
ctio
n
o
f
ad
d
itio
n
al
p
r
e
-
tr
ain
in
g
p
h
ases
with
task
s
clo
s
ely
r
elate
d
to
t
h
e
tar
g
et
d
o
m
ain
f
ac
ilit
ates
s
m
o
o
th
e
r
tr
a
n
s
itio
n
s
an
d
b
etter
p
er
f
o
r
m
an
ce
o
n
th
e
n
ew
task
s
.
5
.
6
.
So
lutio
ns
f
o
r
ha
llu
cina
t
i
o
ns
R
ed
u
ctio
n
o
f
h
allu
ci
n
atio
n
s
in
L
L
Ms
ca
n
b
e
ad
d
r
ess
ed
th
r
o
u
g
h
s
ev
er
al
ap
p
r
o
ac
h
es.
Fi
n
e
-
tu
n
in
g
L
L
Ms
o
n
h
ig
h
-
q
u
ality
,
d
o
m
ai
n
-
s
p
ec
if
ic
d
ata
h
el
p
s
im
p
r
o
v
e
ac
cu
r
ac
y
a
n
d
r
e
d
u
ce
th
e
ch
a
n
ce
s
o
f
g
e
n
er
atin
g
in
co
r
r
ec
t
o
r
f
ab
r
icate
d
in
f
o
r
m
atio
n
.
I
n
co
r
p
o
r
atin
g
R
L
HF
all
o
ws
th
e
m
o
d
el
to
alig
n
m
o
r
e
clo
s
ely
with
h
u
m
an
ex
p
ec
tatio
n
s
an
d
f
ac
tu
al
co
r
r
ec
tn
ess
.
R
L
HF
i
s
a
tr
ain
in
g
a
p
p
r
o
ac
h
wh
er
e
a
n
AI
m
o
d
el
lear
n
s
b
y
r
ec
ei
v
in
g
f
ee
d
b
ac
k
f
r
o
m
h
u
m
an
s
o
n
its
o
u
tp
u
ts
.
I
n
s
tead
o
f
r
ely
in
g
s
o
lely
o
n
p
r
ed
e
f
in
ed
d
atasets
,
R
L
HF
in
co
r
p
o
r
ates
h
u
m
an
ju
d
g
m
en
ts
to
g
u
id
e
t
h
e
m
o
d
el'
s
lear
n
in
g
p
r
o
ce
s
s
.
User
s
ev
alu
ate
th
e
m
o
d
el'
s
r
esp
o
n
s
es,
p
r
o
v
id
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
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2
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t 2
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f
ee
d
b
ac
k
o
n
q
u
ality
,
ac
cu
r
ac
y
,
an
d
r
elev
an
ce
.
T
h
e
m
o
d
el
th
en
u
s
es
th
is
f
ee
d
b
ac
k
to
im
p
r
o
v
e
its
b
eh
av
io
r
,
alig
n
in
g
m
o
r
e
clo
s
ely
with
h
u
m
an
p
r
ef
er
e
n
ce
s
an
d
e
x
p
ec
tat
io
n
s
[
5
5
]
.
T
h
is
iter
ativ
e
p
r
o
ce
s
s
h
elp
s
r
ef
in
e
th
e
m
o
d
el,
r
ed
u
cin
g
e
r
r
o
r
s
,
im
p
r
o
v
in
g
alig
n
m
e
n
t
with
h
u
m
an
g
o
als,
an
d
ad
d
r
ess
in
g
is
s
u
es
lik
e
b
ias
o
r
h
allu
cin
atio
n
s
in
g
e
n
er
ated
o
u
tp
u
ts
.
I
n
teg
r
atin
g
au
to
m
ated
f
ac
t
-
c
h
ec
k
in
g
s
y
s
tem
s
in
to
L
L
Ms
en
s
u
r
es
th
at
g
en
er
ate
d
c
o
n
ten
t
is
cr
o
s
s
-
ch
ec
k
ed
with
v
er
if
ied
d
atab
ases
,
wh
ich
also
h
elp
s
in
m
in
im
izin
g
er
r
o
r
s
.
C
o
n
t
r
o
lled
o
u
tp
u
t
g
en
e
r
atio
n
ca
n
lim
it
th
e
m
o
d
el
’
s
cr
ea
tiv
e
f
r
ee
d
o
m
,
th
u
s
p
r
ev
en
tin
g
s
p
e
cu
lativ
e
o
r
f
alse
in
f
o
r
m
atio
n
.
C
o
m
b
in
in
g
L
L
Ms
with
R
AG
allo
ws
r
ea
l
-
tim
e
ac
ce
s
s
to
tr
u
s
ted
d
ata
s
o
u
r
ce
s
,
g
r
o
u
n
d
i
n
g
th
e
m
o
d
el’
s
r
esp
o
n
s
es
in
f
ac
tu
al
co
n
ten
t.
E
n
h
a
n
cin
g
ex
p
lain
a
b
ilit
y
an
d
tr
an
s
p
ar
en
cy
h
elp
s
tr
ac
e
h
o
w
o
u
tp
u
ts
ar
e
g
en
er
ated
,
en
ab
lin
g
d
ev
elo
p
er
s
t
o
id
en
tif
y
an
d
c
o
r
r
ec
t
h
allu
ci
n
atio
n
s
.
R
eg
u
la
r
u
p
d
ates
to
th
e
m
o
d
el’
s
tr
ain
in
g
d
ata
en
s
u
r
e
r
elev
an
ce
,
w
h
ile
h
u
m
an
-
in
-
th
e
-
lo
o
p
s
y
s
tem
s
in
cr
itical
ap
p
li
ca
tio
n
s
p
r
o
v
id
e
e
x
p
er
t
o
v
er
s
ig
h
t,
f
u
r
th
er
r
ed
u
ci
n
g
th
e
r
is
k
o
f
h
allu
cin
atio
n
s
in
h
ig
h
-
s
tak
es d
o
m
ain
s
lik
e
h
ea
lth
ca
r
e
o
r
law.
6.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
d
is
cu
s
s
es
s
o
m
e
o
f
th
e
p
r
o
b
lem
s
f
ac
ed
b
y
ty
p
ical
L
L
Ms
s
p
ec
if
ic
to
d
o
m
ain
-
r
elate
d
q
u
er
ies,
s
u
ch
as
lack
o
f
d
o
m
a
in
ex
p
er
tis
e,
u
n
d
er
s
tan
d
in
g
s
p
ec
ialized
ter
m
in
o
lo
g
y
,
co
n
tex
t
u
al
u
n
d
er
s
tan
d
i
n
g
,
b
ias,
tr
an
s
f
er
lear
n
in
g
lim
itatio
n
s
,
an
d
h
allu
cin
atio
n
s
.
T
h
e
d
etails
o
f
th
ese
c
h
allen
g
es
ar
e
p
r
esen
ted
alo
n
g
wit
h
s
p
ec
if
ic
in
s
tan
ce
s
f
r
o
m
p
o
p
u
l
ar
L
L
Ms.
So
m
e
s
o
lu
tio
n
s
s
u
c
h
as
f
in
e
tu
n
i
n
g
,
s
lo
w
th
in
k
i
n
g
,
h
u
m
an
f
ee
d
b
ac
k
,
Mo
E
,
k
n
o
wled
g
e
d
is
t
illatio
n
t
ec
h
n
iq
u
es,
task
-
ag
n
o
s
tic
r
ep
r
e
s
en
tatio
n
s
,
cu
r
atio
n
o
f
im
b
ala
n
ce
d
d
atasets
,
an
d
u
s
e
o
f
m
em
o
r
y
-
a
u
g
m
e
n
ted
m
o
d
els ar
e
also
d
is
cu
s
s
ed
f
o
r
m
it
ig
atio
n
o
f
t
h
ese
ch
allen
g
es.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
n
o
f
u
n
d
in
g
in
v
o
lv
ed
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
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