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a
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e
c
o
n
tri
b
u
ti
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s
fo
r
th
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stu
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y
a
re
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i
)
in
tro
d
u
c
e
a
n
e
d
u
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a
ti
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tere
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d
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d
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p
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v
it
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,
a
ss
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ss
m
e
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t
a
n
d
e
v
a
lu
a
ti
o
n
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p
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il
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p
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a
n
d
in
telli
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e
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tu
t
o
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with
il
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stra
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m
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m
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;
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g
iv
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a
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m
a
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c
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tex
t
-
a
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a
n
d
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stra
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m
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a
n
d
wh
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a
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six
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risk
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t
h
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t
g
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it
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c
o
rp
u
s
d
o
wn
t
o
5
0
k
e
y
a
rti
c
les
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a
ll
o
win
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f
o
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m
o
d
ifi
c
a
ti
o
n
s
a
n
d
f
u
tu
re
u
p
d
a
tes
fr
o
m
o
th
e
r
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tere
ste
d
re
se
a
rc
h
e
rs.
In
su
m
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th
e
d
isc
u
s
sio
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a
b
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t
LL
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s
in
e
d
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c
a
ti
o
n
h
a
s
b
e
e
n
p
u
sh
e
d
p
a
st
th
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b
ro
a
d
str
o
k
e
s
t
o
a
situ
a
ti
o
n
w
h
e
re
th
e
re
is
a
c
o
m
p
re
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siv
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a
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lary
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a
t
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a
n
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o
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n
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y
m
a
y
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m
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y
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o
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re
sp
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ly
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e
d
u
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K
ey
w
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s
:
Ap
p
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s
Ar
tific
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in
tellig
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C
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allen
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I
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t
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s
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r
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k
ab
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r
ev
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lu
tio
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in
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in
tellig
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ce
(
AI
)
,
p
ar
ticu
lar
ly
in
th
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f
ield
o
f
lar
g
e
la
n
g
u
a
g
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m
o
d
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(
L
L
Ms)
[
1
]
.
L
L
Ms,
wh
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p
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s
s
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im
m
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s
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co
m
p
u
t
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p
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in
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atasets
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p
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s
in
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(
NL
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p
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o
b
lem
s
[
2
]
–
[
4
]
.
T
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f
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wh
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T
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p
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[
1
]
,
[
5
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,
[
6
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.
B
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m
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d
p
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m
a
n
-
lik
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tex
t
[
7
]
–
[
9
]
.
S
p
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if
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,
m
o
d
els
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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t J Ar
tif
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tell
,
Vo
l.
15
,
No
.
3
,
J
u
n
e
20
26
:
2
1
4
1
-
2
1
5
2
2142
s
u
ch
as
GPT
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3
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d
GPT
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4
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s
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v
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f
in
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tu
n
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g
[
1
0
]
–
[
1
2
]
.
Fu
r
th
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m
o
r
e,
s
p
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m
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s
p
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Fin
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s
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t
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r
s
tan
d
L
L
Ms: wh
at
th
ey
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e,
h
o
w
th
e
y
ca
m
e
to
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e,
an
d
th
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v
an
t
ag
es o
v
er
tr
a
d
itio
n
al
lan
g
u
ag
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m
o
d
els
[
1
3
]
,
[
1
4
]
.
T
h
is
p
ap
er
in
v
esti
g
ates
th
e
in
tr
o
d
u
ctio
n
o
f
g
en
e
r
ativ
e
m
o
d
els,
an
aly
ze
s
L
L
Ms
in
d
etail,
an
d
h
ig
h
lig
h
ts
th
eir
a
d
v
an
ta
g
es
o
v
er
tr
ad
itio
n
al
m
o
d
els,
wh
ich
o
f
ten
s
tr
u
g
g
le
with
c
o
m
p
lex
item
s
lik
e
m
etap
h
o
r
s
an
d
s
ar
ca
s
m
.
I
t
also
ex
am
i
n
es
th
e
lim
itatio
n
s
an
d
ch
allen
g
e
s
o
f
L
L
Ms,
s
u
ch
as
co
m
p
u
tati
o
n
al
d
em
a
n
d
s
,
d
ata
p
r
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ac
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,
an
d
"
h
allu
cin
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s
"
.
Fu
r
th
er
m
o
r
e,
it
e
x
p
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t
h
e
im
p
licatio
n
s
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d
way
s
L
L
Ms
ca
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aid
th
e
ed
u
ca
tio
n
al
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n
tex
t b
y
f
ac
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atin
g
p
er
s
o
n
aliza
tio
n
,
ad
ap
tiv
it
y
,
an
d
i
n
tellig
en
t tu
to
r
i
n
g
.
2.
M
E
T
H
O
D
T
h
e
r
ev
iew
p
r
o
ce
s
s
was
u
n
d
er
tak
en
with
a
s
y
s
tem
atic
s
tep
-
by
-
s
tep
ap
p
r
o
ac
h
to
en
s
u
r
e
th
at
all
r
esear
ch
q
u
esti
o
n
s
an
d
o
th
er
ex
am
in
ed
to
p
ics
o
n
L
L
Ms
an
d
th
eir
ap
p
licatio
n
to
ed
u
ca
tio
n
wer
e
co
m
p
r
eh
e
n
s
iv
ely
r
ev
iewe
d
.
T
h
e
p
r
o
ce
s
s
,
as
o
u
tlin
ed
in
Fi
g
u
r
e
1
,
b
eg
an
with
a
liter
atu
r
e
s
ea
r
ch
p
h
ase.
A
b
r
o
ad
r
an
g
e
o
f
ac
a
d
em
ic
s
o
u
r
ce
s
was
u
n
co
v
e
r
ed
u
s
in
g
s
p
ec
if
ic
k
ey
wo
r
d
s
s
u
ch
as,
"L
L
M
s
"
,
"lar
g
e
lan
g
u
ag
e
m
o
d
els"
,
"g
en
e
r
ativ
e
AI
"
,
"e
d
u
ca
tio
n
"
,
an
d
"e
-
lear
n
in
g
"
.
T
h
is
s
ea
r
ch
r
esu
lted
i
n
a
c
o
n
s
id
er
ab
le
n
u
m
b
er
o
f
ar
ticles
wh
ich
wer
e
r
ev
iewe
d
iter
ativ
ely
b
ased
o
n
th
eir
r
ele
v
an
ce
,
ac
ad
em
ic
r
i
g
o
r
,
a
n
d
co
n
tr
ib
u
tio
n
s
to
to
p
ic.
Af
ter
th
is
r
ev
iew,
an
in
itial
s
e
t
o
f
5
0
ar
ticles
wer
e
id
en
tifie
d
an
d
s
elec
ted
th
at
wer
e
d
ee
m
ed
th
e
co
r
e
f
o
r
th
e
s
tu
d
y
.
T
h
ese
ar
ticles
estab
lis
h
ed
a
b
ase
o
f
k
n
o
wled
g
e
o
n
th
e
tech
n
ical
wo
r
k
in
g
s
o
f
L
L
Ms
an
d
th
e
im
p
licatio
n
s
o
f
L
L
Ms b
ey
o
n
d
th
e
tech
n
ical
asp
ec
ts
with
in
ed
u
ca
tio
n
.
Fig
u
r
e
1
.
Ov
e
r
v
iew
o
f
th
e
r
ese
ar
ch
m
eth
o
d
o
lo
g
y
f
o
r
ex
p
lo
r
in
g
L
L
Ms a
n
d
th
eir
ed
u
ca
tio
n
al
ap
p
licatio
n
s
Fo
llo
win
g
th
is
,
r
esear
ch
en
ter
ed
a
p
h
ase
o
f
g
en
er
al
in
q
u
ir
y
wh
er
e
r
esear
ch
q
u
esti
o
n
s
wer
e
d
ev
elo
p
e
d
to
p
r
o
m
o
te
th
e
d
etailed
ex
p
l
o
r
atio
n
o
f
th
e
to
p
ic.
T
h
e
in
q
u
i
r
y
b
eg
an
with
in
v
esti
g
atin
g
g
en
er
ativ
e
AI
as
an
u
m
b
r
ella
ter
m
to
u
n
d
er
s
tan
d
a
s
p
ec
ts
s
u
ch
as
its
o
r
ig
in
s
,
wh
e
n
g
en
e
r
ativ
e
AI
s
tar
ted
to
g
ai
n
tr
ac
tio
n
,
an
d
h
o
w
its
g
r
o
wth
a
n
d
d
ev
elo
p
m
en
t
lead
to
th
e
em
er
g
e
n
ce
o
f
L
L
Ms.
T
h
is
in
q
u
ir
y
also
ex
am
in
ed
a
h
is
to
r
ical
p
er
s
p
ec
tiv
e
o
f
AI
,
in
o
r
d
er
t
o
r
ec
o
g
n
ize
k
e
y
d
ev
elo
p
m
en
t
p
er
io
d
s
wh
ich
,
co
n
tr
ib
u
te
to
th
e
ev
o
lu
tio
n
.
T
h
e
q
u
esti
o
n
s
b
ein
g
ask
ed
d
u
r
in
g
th
is
in
q
u
ir
y
also
ass
is
ted
to
clar
if
y
h
o
w
L
L
Ms
f
it
in
to
t
h
e
o
v
er
all
v
ar
ia
b
le
lan
d
s
ca
p
e
o
f
AI
tech
n
o
lo
g
ies
th
at
ca
n
b
e
co
n
s
id
er
e
d
f
o
r
s
p
ec
if
ic
in
q
u
ir
y
m
o
v
e
m
en
ts
.
T
h
e
th
ir
d
s
tep
o
f
th
e
m
eth
o
d
o
l
o
g
y
p
r
esen
ted
a
cl
o
s
er
an
aly
s
is
o
f
L
L
Ms
an
d
th
ei
r
ca
teg
o
r
izatio
n
.
As
th
er
e
ar
e
a
m
u
ltit
u
d
e
o
f
L
L
Ms
in
to
d
ay
’
s
m
ar
k
etp
lace
,
th
e
r
esear
ch
aim
s
to
ex
p
lo
r
e
th
e
ca
teg
o
r
izatio
n
o
f
L
L
Ms,
o
r
i
n
o
th
er
wo
r
d
s
h
o
w
L
L
Ms a
r
e
g
r
o
u
p
ed
.
T
h
e
p
r
o
ce
s
s
co
n
s
is
ted
o
f
ca
teg
o
r
izin
g
L
L
Ms a
n
d
an
aly
zin
g
h
o
w
th
e
y
ar
e
g
r
o
u
p
ed
b
y
s
ize,
ar
ch
itectu
r
e,
an
d
ap
p
licatio
n
.
On
ce
th
e
ca
teg
o
r
izatio
n
was
c
o
m
p
lete,
th
e
r
esear
ch
b
e
g
an
c
o
m
p
ar
in
g
tr
ad
itio
n
al
lan
g
u
a
g
e
m
o
d
els
to
L
L
Ms.
T
h
e
co
m
p
a
r
ativ
e
m
eth
o
d
o
lo
g
y
aim
s
to
e
x
p
o
s
e
th
e
li
m
itatio
n
s
o
f
tr
ad
itio
n
al
la
n
g
u
ag
e
m
o
d
els,
wh
ich
h
av
e
d
o
m
i
n
ated
th
e
f
ield
,
a
n
d
h
o
w
L
L
Ms
h
av
e
ad
d
r
ess
ed
th
o
s
e
lim
itatio
n
s
.
I
n
p
ar
ticu
lar
,
c
o
m
p
ar
is
o
n
f
o
cu
s
ed
o
n
L
L
Ms'
ab
ilit
ies
to
h
an
d
le
l
ar
g
er
d
ata
s
ets
an
d
p
er
f
o
r
m
m
o
r
e
co
m
p
lex
task
s
wh
ile
y
ield
in
g
m
o
r
e
ac
cu
r
ate
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
N
a
vig
a
tin
g
th
e
n
ew fr
o
n
tier
:
l
a
r
g
e
la
n
g
u
a
g
e
m
o
d
els a
n
d
th
e
ir
imp
lica
tio
n
s
fo
r
ed
u
ca
tio
n
(
La
ila
B
o
u
llo
u
s
)
2143
r
esu
lts
.
Utilizin
g
a
c
o
m
p
ar
ati
v
e
ap
p
r
o
ac
h
elu
ci
d
ates
th
e
p
r
o
g
r
ess
in
th
e
f
ield
o
f
s
ec
o
n
d
l
an
g
u
ag
e
ac
q
u
is
itio
n
an
d
h
ig
h
lig
h
ts
wh
y
L
L
Ms n
ec
ess
itate
m
er
it a
s
a
n
ew
an
d
in
n
o
v
ativ
e
ap
p
r
o
ac
h
in
th
e
f
ield
o
f
ed
u
ca
tio
n
.
Af
ter
illu
s
tr
atin
g
th
e
s
tr
en
g
th
s
o
f
L
L
Ms,
th
e
co
m
p
ar
is
o
n
s
h
if
ted
to
lim
itatio
n
s
an
d
ch
allen
g
es
o
f
L
L
Ms.
W
h
ile
L
L
M
s
ar
e
clea
r
l
y
an
en
h
an
ce
m
e
n
t
to
s
ec
o
n
d
lan
g
u
ag
e
ac
q
u
is
itio
n
,
L
L
Ms
s
h
o
u
ld
n
o
t
b
e
s
ee
n
as
p
er
f
ec
t.
T
h
is
p
ar
t
o
f
th
e
r
ev
i
ew
p
r
o
p
o
s
es
a
cr
itical
ev
alu
a
tio
n
o
f
th
e
ch
allen
g
es
o
f
L
L
Ms.
E
x
am
in
in
g
th
e
ch
allen
g
es p
r
esen
ts
a
b
alan
ce
d
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
p
o
ten
tial
an
d
lim
itatio
n
s
o
f
L
L
Ms.
T
h
e
last
p
o
r
tio
n
o
f
th
e
m
et
h
o
d
o
lo
g
y
lo
o
k
s
m
o
r
e
clo
s
ely
at
L
L
Ms
an
d
e
d
u
ca
tio
n
.
T
h
e
r
e
v
iew
r
aises
is
s
u
es
o
f
h
o
w
L
L
Ms
ar
e
p
er
c
eiv
ed
in
ed
u
ca
tio
n
al
en
v
ir
o
n
m
en
ts
an
d
wh
eth
er
th
e
r
e
is
a
f
o
cu
s
o
n
e
-
lea
r
n
in
g
en
v
ir
o
n
m
en
ts
.
Fin
ally
,
th
is
an
aly
s
is
p
o
in
ts
to
p
o
s
s
ib
le
o
u
tco
m
es
f
o
r
L
L
Ms
an
d
also
p
r
o
v
id
es
is
s
u
es
in
L
L
M
s
wh
er
e
d
ev
elo
p
m
en
t a
n
d
r
esear
ch
ar
e
n
ee
d
ed
to
b
etter
s
tu
d
en
t
s
an
d
ed
u
ca
to
r
s
with
L
L
M
to
o
ls
.
3.
E
VO
L
U
T
I
O
N
O
F
G
E
N
E
RA
T
I
V
E
M
O
D
E
L
S IN
AR
T
I
F
I
CIAL
I
NT
E
L
L
I
G
E
NC
E
T
h
e
s
to
r
y
o
f
th
e
g
e
n
er
ativ
e
m
o
d
el,
wh
ic
h
is
illu
s
tr
ated
i
n
Fig
u
r
e
2
,
b
eg
in
s
wh
e
n
Al
an
T
u
r
i
n
g
p
r
o
p
o
s
ed
T
u
r
i
n
g
m
ac
h
in
es
in
1
9
3
6
wh
en
h
e
b
u
ilt
th
e
m
o
d
els
ca
p
ab
le
o
f
im
itatin
g
an
d
r
ep
r
o
d
u
cin
g
lin
ea
r
d
ata.
Hid
d
en
Ma
r
k
o
v
m
o
d
els
(
HM
M)
b
eg
an
to
a
r
is
e
in
1
9
6
0
th
at
p
r
o
v
id
e
d
a
n
ew
p
r
o
b
a
b
ilis
tic
-
b
ased
p
r
o
ce
s
s
to
in
f
er
an
d
p
r
ed
ict
s
eq
u
en
ce
s
.
1
9
8
2
m
ar
k
s
th
e
in
tr
o
d
u
ctio
n
o
f
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
s
(
R
NNs),
u
n
lo
ck
in
g
th
e
ab
ilit
y
to
r
em
e
m
b
er
p
r
ev
i
o
u
s
in
p
u
ts
.
Af
ter
war
d
s
,
th
e
lo
n
g
s
h
o
r
t
-
te
r
m
m
em
o
r
y
(
L
STM
)
was
s
u
g
g
ested
in
1
9
9
7
,
r
esu
ltin
g
in
f
ac
ilit
atin
g
v
ar
io
u
s
task
s
,
in
clu
d
in
g
tim
e
s
er
ies
p
r
ed
ictio
n
,
b
y
o
v
er
co
m
in
g
th
e
ch
allen
g
es
r
elate
d
to
lo
n
g
-
ter
m
d
ep
en
d
en
cies.
Fig
u
r
e
2
.
A
tim
elin
e
o
f
g
e
n
er
a
tiv
e
AI
m
o
d
els s
tar
tin
g
f
r
o
m
T
u
r
in
g
m
ac
h
in
es in
1
9
3
6
to
m
o
d
er
n
GPT
ar
ch
itectu
r
es
T
h
e
r
is
e
o
f
tr
an
s
f
o
r
m
er
s
s
tar
ted
with
th
e
p
r
o
p
o
s
itio
n
o
f
a
n
ew
f
r
am
ew
o
r
k
in
2
0
1
3
t
h
at
h
elp
ed
g
en
er
ate
n
ew
d
ata
b
ased
o
n
a
tr
ain
in
g
s
et;
th
e
ty
p
e
o
f
g
en
er
ated
d
ata
v
ar
ies
f
r
o
m
tex
tu
al,
i
m
ag
er
y
,
a
u
d
io
,
an
d
m
o
r
e.
Usi
n
g
n
e
u
r
al
-
n
etwo
r
k
-
b
ased
g
e
n
er
ativ
e
m
o
d
els
s
u
ch
as
th
e
v
ar
ian
t
au
to
-
e
n
c
o
d
er
s
(
VAE
s
)
an
d
g
en
er
ativ
e
-
a
d
v
er
s
ar
ial
n
etwo
r
k
s
(
GANs)
.
T
h
e
r
ev
o
lu
tio
n
ar
y
u
p
h
ea
v
al
em
er
g
ed
with
th
e
p
u
b
licatio
n
o
f
th
e
p
ap
er
“Atten
tio
n
is
all
y
o
u
n
e
ed
”
in
2
0
1
7
[
2
]
,
wh
er
e
Go
o
g
le
p
r
esen
ted
th
e
ar
ch
itectu
r
e
o
f
tr
an
s
f
o
r
m
er
s
an
d
th
eir
p
o
wer
th
at
lies
in
th
e
s
e
lf
-
atten
tio
n
m
ec
h
an
is
m
an
d
it
s
ab
ilit
y
to
id
en
tify
lo
n
g
-
r
an
g
e
d
ep
en
d
en
cies
an
d
tr
ac
e
co
n
n
ec
tio
n
s
with
in
th
e
d
ata
[
1
]
.
T
h
e
f
o
llo
win
g
y
ea
r
th
e
s
u
g
g
ested
f
r
am
ewo
r
k
was
im
p
lem
en
ted
in
to
L
L
Ms.
Go
o
g
le
in
tr
o
d
u
ce
d
B
E
R
T
,
an
ex
ten
s
iv
e
la
n
g
u
a
g
e
m
o
d
el,
its
p
o
wer
lies
in
its
p
r
o
f
icien
cy
i
n
c
o
m
p
r
e
h
en
d
in
g
t
h
e
co
n
tex
t
o
f
wo
r
d
s
in
a
s
en
ten
ce
[
7
]
.
Op
en
AI
p
r
es
en
ted
its
L
L
M
GPT
with
its
ab
ilit
y
to
p
r
o
d
u
ce
h
u
m
an
-
lik
e
tex
t
[
8
]
.
W
ith
th
e
ap
p
ar
itio
n
o
f
DAL
L
-
E
in
2
0
2
1
,
th
e
wo
r
ld
k
n
ew
th
at
tr
an
s
f
o
r
m
er
s
ca
p
ab
ilit
ies
ex
ten
d
NL
P
an
d
ca
n
b
e
u
s
ed
f
o
r
im
a
g
e
g
en
er
atio
n
f
r
o
m
te
x
tu
al
p
r
o
m
p
ts
.
Op
en
AI
,
f
o
u
n
d
ed
i
n
Dec
em
b
er
2
0
1
5
,
h
as
co
n
tr
ib
u
ted
m
ain
ly
to
th
e
a
d
v
an
ce
m
e
n
t
o
f
L
L
Ms
an
d
in
f
lu
en
ce
d
th
e
lan
d
s
ca
p
e
o
f
AI
r
esear
ch
an
d
ap
p
licatio
n
s
.
T
h
ey
f
o
c
u
s
ed
o
n
b
u
ild
in
g
g
e
n
er
ativ
e
m
o
d
els,
s
tar
tin
g
f
r
o
m
GPT
-
1
to
lau
n
ch
in
g
C
h
atGPT
in
2
0
2
2
.
T
h
e
p
o
p
u
lar
ity
o
f
L
L
Ms
h
as
in
cr
ea
s
ed
ex
p
o
n
en
tially
af
ter
th
e
lau
n
ch
o
f
C
h
atGPT
,
as
it
h
as
g
ain
ed
m
illi
o
n
s
o
f
u
s
er
s
o
v
er
a
f
ew
w
ee
k
s
af
ter
its
r
elea
s
e
th
an
k
s
to
h
ig
h
p
er
f
o
r
m
an
ce
with
u
n
s
ee
n
task
s
b
ased
o
n
its
ab
ilit
y
to
g
en
er
alize
f
r
o
m
ta
k
in
g
a
task
d
escr
ip
tio
n
as
in
p
u
t
an
d
a
f
ew
ex
am
p
les.
I
t
d
o
esn
’
t
r
eq
u
ir
e
an
y
task
-
s
p
ec
if
ic
tr
ain
in
g
,
wh
ich
o
p
en
s
th
e
d
o
o
r
to
a
n
ew
er
a
in
AI
,
in
tr
o
d
u
cin
g
wh
at
we
r
ef
er
to
as
ar
tific
ial
g
en
er
al
in
tellig
en
ce
[
1
0
]
.
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.
15
,
No
.
3
,
J
u
n
e
20
26
:
2
1
4
1
-
2
1
5
2
2144
GPT
m
o
d
els
co
m
b
in
e
u
n
s
u
p
er
v
is
ed
p
r
e
-
tr
ain
i
n
g
an
d
s
u
p
er
v
i
s
ed
f
in
e
-
tu
n
n
in
g
to
g
en
e
r
ate
h
u
m
an
-
lik
e
an
s
wer
s
.
GPT
-
3
is
a
v
er
s
io
n
o
f
GPT
m
o
d
els
b
u
ilt
with
1
7
5
b
illi
o
n
p
ar
am
ete
r
s
,
tr
ain
ed
o
n
a
lar
g
e
co
r
p
u
s
co
n
tain
in
g
o
f
tex
tu
al
d
ata
f
r
o
m
web
p
ag
es,
b
o
o
k
s
,
ar
ticles,
an
d
s
o
cial
m
ed
ia
[
3
]
,
[
4
]
,
[
1
0
]
.
Op
en
AI
in
tr
o
d
u
ce
d
GPT
-
4
,
an
ad
v
an
ce
d
v
e
r
s
io
n
o
f
GPT,
in
Ma
r
ch
2
0
2
3
with
a
m
ass
iv
e
n
u
m
b
er
o
f
p
ar
am
et
er
s
th
at
g
o
es
u
p
to
1
.
7
6
tr
illi
o
n
p
ar
am
eter
s
;
it
ca
n
p
r
o
ce
s
s
b
o
t
h
im
a
g
e
a
n
d
tex
t
in
p
u
ts
.
GPT
-
4
ca
p
ab
ilit
ies
g
o
b
ey
o
n
d
g
e
n
er
atin
g
h
u
m
an
-
lik
e
tex
t,
th
is
m
o
d
el
f
a
m
ily
h
as f
u
r
th
e
r
p
u
s
h
ed
th
e
p
e
r
f
o
r
m
a
n
ce
r
esu
lts
in
v
ar
io
u
s
NL
P task
s
[
1
0
]
.
T
h
e
latest
v
er
s
io
n
o
f
C
h
atGPT
is
C
h
atGPT
-
4
o
(
th
e
last
‘
o
’
s
tan
d
s
f
o
r
‘
o
m
n
i’
)
,
r
elea
s
ed
o
n
Ma
y
1
3
,
2
0
2
4
,
b
y
th
e
g
ia
n
t
Op
en
AI
.
T
h
is
v
er
s
io
n
ex
ten
d
s
t
h
e
ca
p
ab
i
liti
es
o
f
C
h
atGPT
-
4
.
B
ased
o
n
u
s
er
e
x
p
er
ien
ce
s
,
it
h
as
b
ee
n
co
n
clu
d
ed
th
at
C
h
atGPT
-
4
o
is
f
aster
an
d
s
ig
n
if
ican
tly
b
etter
at
g
en
er
atin
g
h
u
m
an
-
lik
e
r
esp
o
n
s
es
th
an
C
h
atGPT
-
4
.
Nu
m
er
o
u
s
v
ir
al
v
id
eo
s
h
av
e
s
h
o
wca
s
ed
C
h
atGPT
-
4
o
'
s
ca
p
ac
ity
to
d
el
iv
er
r
esp
o
n
s
es
in
a
ch
atty
an
d
o
cc
asio
n
ally
f
lirta
t
io
u
s
to
n
e.
Ad
d
itio
n
ally
,
C
h
at
GPT
-
4
o
f
ea
tu
r
es
ca
p
a
b
ilit
ies
s
u
ch
as
r
ea
d
in
g
an
d
d
is
cu
s
s
in
g
im
ag
es,
tr
an
s
latin
g
lan
g
u
ag
es,
id
e
n
tify
in
g
em
o
tio
n
s
f
r
o
m
v
is
u
al
e
x
p
r
ess
io
n
s
,
an
d
r
em
em
b
er
in
g
p
r
ev
io
u
s
p
r
o
m
p
ts
.
C
h
atGPT
-
4
o
h
as
d
em
o
n
s
tr
ated
n
ew
ca
p
a
b
ilit
ies
with
th
e
p
o
ten
tial
to
r
e
v
o
lu
tio
n
ize
v
ar
io
u
s
f
ield
s
,
in
clu
d
in
g
ed
u
ca
tio
n
an
d
e
-
lear
n
in
g
[
1
5
]
.
4.
L
ARG
E
L
ANG
UAG
E
M
O
D
E
L
:
D
E
F
I
NI
T
I
O
N
,
T
YP
E
S
,
AND
CO
M
P
AR
I
SO
N
4
.
1
.
Def
ini
t
io
n
L
L
Ms
ar
e
s
p
ec
if
ic
im
p
lem
en
t
atio
n
s
o
f
tr
an
s
f
o
r
m
er
-
b
ased
m
o
d
els
tr
ain
ed
o
n
a
m
ass
iv
e
am
o
u
n
t
o
f
tex
tu
al
d
ata,
th
eir
f
o
u
n
d
atio
n
lies
in
th
eir
ad
v
an
ce
d
p
r
o
ce
s
s
in
g
ca
p
ab
ilit
ies
an
d
ad
v
an
ce
d
alg
o
r
ith
m
s
.
As
a
r
esu
lt,
L
L
Ms
ca
n
ca
p
tu
r
e,
tr
a
n
s
late,
ex
p
ec
t,
a
n
d
p
r
o
d
u
ce
tex
tu
al
co
n
te
n
t
an
d
o
t
h
er
f
o
r
m
s
(
im
ag
es
an
d
v
o
ice
n
o
tes).
Als
o
,
th
e
y
ar
e
f
lex
i
b
le
ac
r
o
s
s
d
iv
er
s
e
d
o
m
ai
n
s
[
2
]
.
L
L
Ms
ar
e
k
n
o
wn
f
o
r
ac
h
iev
i
n
g
h
ig
h
p
e
r
f
o
r
m
an
ce
in
m
an
y
n
at
u
r
al
lan
g
u
ag
e
ap
p
licatio
n
s
with
o
u
t
task
-
s
p
ec
if
ic
tr
ain
in
g
[
1
0
]
.
L
L
Ms
f
u
n
ctio
n
s
im
p
ly
b
y
tr
ea
tin
g
an
y
NL
P
task
as
an
o
r
ien
ted
tex
t
g
en
er
atio
n
task
an
d
g
en
er
atin
g
o
u
tp
u
t
b
ased
o
n
th
e
in
p
u
t
p
r
o
m
p
t,
th
is
last
o
n
e
d
escr
ib
es th
e
c
o
n
d
itio
n
s
t
h
at
s
h
o
u
ld
b
e
s
atis
f
ied
with
th
e
g
en
er
ated
o
u
tp
u
t
[
1
0
]
.
4
.
2
.
T
y
pes
o
f
la
r
g
e
la
ng
ua
g
e
m
o
dels
T
h
er
e
ar
e
d
if
f
er
e
n
t
ty
p
es
o
f
L
L
Ms,
wh
ich
ca
n
b
e
ca
teg
o
r
iz
ed
in
to
f
iv
e
m
ain
g
r
o
u
p
s
.
T
h
e
s
e
g
r
o
u
p
s
in
clu
d
e
p
r
e
-
tr
ain
ed
an
d
f
in
e
-
t
u
n
ed
m
o
d
els,
en
c
o
d
er
-
d
ec
o
d
e
r
m
o
d
els,
m
u
ltil
in
g
u
al
m
o
d
el
s
,
d
o
m
ain
-
s
p
ec
if
ic
m
o
d
els,
an
d
a
u
to
r
e
g
r
ess
iv
e
lan
g
u
ag
e
m
o
d
els.
T
h
is
ca
teg
o
r
iz
atio
n
is
illu
s
tr
ated
in
Fig
u
r
e
3
.
Fig
u
r
e
3
.
C
ateg
o
r
izatio
n
o
f
L
L
Ms b
ased
o
n
ar
c
h
itectu
r
e,
ap
p
licatio
n
,
an
d
lan
g
u
a
g
es
4
.
2
.
1
.
P
re
-
t
r
a
ined a
nd
f
ine
-
t
un
ed
m
o
dels
Fo
llo
win
g
th
e
m
ass
iv
e
ac
h
iev
em
en
t
o
f
p
r
e
-
tr
ain
e
d
im
a
g
e
m
o
d
els
in
co
m
p
u
ter
v
is
io
n
(
VGGN
et
an
d
A
lex
Net
as
ex
am
p
les)
[
1
6
]
,
N
L
P
r
esear
ch
er
s
g
o
t
in
s
p
ir
e
d
to
d
ev
elo
p
p
r
e
-
tr
ain
ed
m
o
d
els
f
o
r
NL
P
task
s
,
b
u
ilt
b
ased
o
n
tr
a
n
s
f
o
r
m
er
s
an
d
s
elf
-
s
u
p
er
v
is
ed
lear
n
in
g
,
a
n
d
tr
ai
n
ed
o
n
a
lar
g
e
c
o
r
p
u
s
th
ey
ca
n
b
e
tailo
r
ed
t
o
f
it
g
o
al
-
o
r
ie
n
ted
task
s
with
c
o
n
s
tr
ain
ed
lab
eled
d
ata.
T
h
e
ev
o
lu
tio
n
o
f
p
r
e
-
tr
ai
n
ed
la
n
g
u
a
g
e
m
o
d
els
(
PLM
s
)
b
eg
in
s
with
Go
o
g
le’
s
f
am
o
u
s
m
o
d
el
B
E
R
T
an
d
Op
e
n
AI
’
s
GPT
-
1
[
9
]
.
Fro
m
th
er
e,
m
an
y
PLM
s
wer
e
d
ev
elo
p
e
d
,
s
u
ch
as
r
o
b
u
s
tly
o
p
tim
ized
B
E
R
T
p
r
etr
ai
n
in
g
ap
p
r
o
ac
h
(
R
o
B
E
R
T
a)
,
XL
Ne
t,
b
id
ir
ec
tio
n
al
an
d
au
to
-
r
eg
r
ess
iv
e
tr
an
s
f
o
r
m
er
s
(
B
AR
T
)
,
an
d
E
lectr
a
[
9
]
.
4
.
2
.
2
.
E
nco
der
-
deco
der
m
o
dels
PLM
's
jo
u
r
n
ey
p
r
o
g
r
ess
ed
in
th
r
ee
s
tag
es:
en
c
o
d
er
-
b
ased
m
o
d
els,
d
ec
o
d
er
-
b
ased
m
o
d
els,
an
d
en
co
d
er
-
d
ec
o
d
er
m
o
d
els.
Val
iy
ev
et
a
l.
[
1
7
]
e
x
p
lain
s
th
e
en
co
d
e
r
-
d
ec
o
d
er
ar
ch
itectu
r
e
co
n
s
is
ts
o
f
b
o
th
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
N
a
vig
a
tin
g
th
e
n
ew fr
o
n
tier
:
l
a
r
g
e
la
n
g
u
a
g
e
m
o
d
els a
n
d
th
e
ir
imp
lica
tio
n
s
fo
r
ed
u
ca
tio
n
(
La
ila
B
o
u
llo
u
s
)
2145
en
co
d
er
an
d
d
ec
o
d
e
r
m
o
d
u
les;
th
e
en
co
d
er
m
o
d
u
le
is
an
em
b
ed
d
in
g
o
f
en
co
d
er
lay
er
s
,
an
d
ea
ch
en
co
d
e
r
lay
er
h
as
s
elf
-
atten
tio
n
an
d
f
e
ed
-
f
o
r
war
d
n
etwo
r
k
s
.
T
h
e
d
ec
o
d
er
m
o
d
u
le
is
an
em
b
e
d
d
in
g
o
f
d
ec
o
d
er
lay
er
s
;
ea
ch
lay
er
is
eq
u
ip
p
e
d
with
a
s
elf
-
atten
tio
n
m
ec
h
an
is
m
,
m
ask
ed
s
elf
-
atten
tio
n
,
an
d
f
ee
d
-
f
o
r
war
d
n
etwo
r
k
s
.
So
m
e
en
co
d
er
-
d
ec
o
d
er
m
o
d
el
s
ar
e
k
n
o
wn
f
o
r
th
eir
h
ig
h
p
e
r
f
o
r
m
a
n
ce
in
n
atu
r
al
lan
g
u
ag
e
g
en
er
atio
n
task
s
,
wh
er
ea
s
o
th
er
s
ar
e
k
n
o
wn
f
o
r
p
er
f
o
r
m
i
n
g
well
in
b
o
th
g
en
er
atio
n
an
d
lan
g
u
a
g
e
u
n
d
er
s
tan
d
in
g
task
s
.
Ma
r
ian
MT
is
an
ex
am
p
le
o
f
s
u
ch
a
m
o
d
el
b
esid
es
tex
t
-
to
-
tex
t
tr
an
s
f
er
t
r
an
s
f
o
r
m
e
r
(
T
5
)
,
B
AR
T
,
an
d
n
o
lan
g
u
ag
e
lef
t
b
eh
in
d
(
NL
L
B
)
[
9
]
.
4
.
2
.
3
.
M
ultiling
ua
l m
o
dels
T
h
e
p
r
e
-
tr
ain
e
d
L
L
Ms
h
av
e
k
n
o
wn
a
v
ast
s
u
cc
ess
in
th
e
E
n
g
lis
h
lan
g
u
a
g
e,
wh
ic
h
in
s
p
ir
ed
r
esear
ch
er
s
to
d
e
v
elo
p
L
L
Ms
f
o
r
o
th
er
lan
g
u
ag
es
a
n
d
m
u
ltil
in
g
u
al
L
L
Ms.
So
m
e
o
f
t
h
e
m
o
s
t
wid
ely
k
n
o
wn
an
d
u
s
ed
ar
e
m
u
ltil
in
g
u
al
b
id
ir
ec
tio
n
al
en
co
d
er
r
e
p
r
esen
tat
io
n
s
f
r
o
m
tr
an
s
f
o
r
m
er
s
(
m
B
E
R
T
)
,
cr
o
s
s
-
lin
g
u
al
lan
g
u
ag
e
m
o
d
el
(
XL
M
)
,
XL
M
-
R
o
B
E
R
T
a
(
XL
M
-
R
)
,
m
u
ltil
in
g
u
al
tex
t
-
to
-
tex
t
t
r
an
s
f
er
t
r
an
s
f
o
r
m
er
(
m
T
5
)
,
m
u
ltil
in
g
u
al
d
ec
o
d
in
g
-
en
h
an
ce
d
B
E
R
T
w
ith
d
is
en
tan
g
led
atten
tio
n
(
m
DeBERTa)
,
B
ig
Scien
ce
lar
g
e
o
p
en
-
s
cien
ce
o
p
en
-
ac
ce
s
s
m
u
l
tili
n
g
u
al
lan
g
u
ag
e
m
o
d
el
(
B
L
OOM
)
,
an
d
B
L
OOM
(
in
s
tr
u
ct
io
n
-
tu
n
e
d
v
e
r
s
io
n
)
(
B
L
OOM
Z
)
[
9
]
,
[
1
0
]
.
T
h
er
e
ar
e
also
f
am
o
u
s
b
ilin
g
u
al
L
L
Ms,
in
clu
d
in
g
J
ais
Ar
ab
ic
-
Ar
ab
ic
(
J
AI
S)
LLM
m
o
d
el
(
f
o
r
E
n
g
lis
h
an
d
Ar
ab
ic
)
an
d
g
en
er
al
lan
g
u
ag
e
m
o
d
el
(
GL
M)
(
f
o
r
E
n
g
lis
h
a
n
d
C
h
in
e
s
e)
[
1
0
]
.
4
.
2
.
4
.
Do
ma
in
-
s
pecif
ic
m
o
dels
Dep
ar
tin
g
f
r
o
m
lo
w
p
er
f
o
r
m
a
n
ce
o
f
g
en
er
al
-
d
o
m
ain
L
L
Ms
in
task
an
d
d
o
m
ain
-
s
p
ec
if
ic
p
r
o
b
lem
s
,
r
esear
ch
er
s
s
h
if
ted
t
h
eir
f
o
cu
s
to
d
ev
el
o
p
in
g
d
o
m
ain
-
s
p
ec
i
f
ic
m
o
d
els
in
v
a
r
io
u
s
d
o
m
ain
s
,
f
o
r
ex
am
p
le,
in
ed
u
ca
tio
n
lik
e
C
h
atGPT
.
I
n
h
ea
lth
ca
r
e,
th
e
Me
d
-
PaL
M
wa
s
d
esig
n
ed
to
d
eliv
e
r
to
p
-
tier
an
s
wer
s
to
m
ed
ical
q
u
esti
o
n
s
[
9
]
,
wh
ile
in
f
i
n
an
ce
,
Fin
GPT
an
d
B
lo
o
m
b
er
g
GPT
em
er
g
ed
[
1
0
]
,
a
n
d
in
law,
Sa
u
lLM
-
7
B
L
L
M
was
tailo
r
ed
f
o
r
t
h
e
leg
al
d
o
m
ai
n
[
1
8
]
.
Ad
d
itio
n
ally
,
in
s
o
cial
m
ed
ia,
s
o
cial
m
ed
ia
p
r
e
-
tr
ai
n
ed
B
E
R
T
(
So
B
er
t)
,
a
L
L
M
tr
ain
ed
o
n
Stack
Ov
er
f
lo
w
d
ata
f
o
r
an
s
wer
in
g
q
u
esti
o
n
s
r
elate
d
to
p
r
o
g
r
am
m
in
g
an
d
s
o
f
twar
e
en
g
in
ee
r
in
g
[
1
9
]
,
r
ep
r
esen
ts
th
is
s
p
ec
ialized
ap
p
r
o
ac
h
.
4
.
2
.
5
.
Aut
o
re
g
re
s
s
iv
e
la
ng
ua
g
e
m
o
dels
Au
to
-
r
eg
r
ess
iv
e
L
L
Ms
ar
e
ad
v
an
ce
d
m
o
d
els
th
at
ca
n
g
en
er
ate
tex
t
b
y
p
r
ed
ictin
g
th
e
n
ex
t
to
k
en
is
cr
ea
ted
b
ased
o
n
p
r
ev
io
u
s
ly
g
en
er
ated
to
k
e
n
s
.
T
h
ese
m
o
d
el
s
,
s
u
ch
as
au
to
r
eg
r
ess
iv
e
m
o
d
els,
ex
ce
l
at
task
s
lik
e
lan
g
u
ag
e
g
en
er
atio
n
an
d
co
m
p
letio
n
b
y
lev
e
r
ag
in
g
lan
g
u
ag
e
p
atter
n
s
lear
n
ed
f
r
o
m
tr
ain
i
n
g
d
ata
.
E
x
am
p
les
o
f
au
to
-
r
eg
r
ess
iv
e
L
L
Ms
in
clu
d
e
GPT
m
o
d
els
d
ev
elo
p
ed
b
y
Op
e
n
AI
;
GPT
-
3
,
f
o
r
ex
am
p
le
in
clu
d
e
s
1
7
5
B
p
ar
am
eter
s
tr
ain
ed
u
s
in
g
a
co
n
s
id
er
ab
le
co
r
p
u
s
co
n
t
ain
in
g
tex
t
f
r
o
m
v
ar
io
u
s
r
eso
u
r
ce
s
s
u
ch
as
web
p
ag
es,
W
ik
ip
ed
ia,
an
d
b
o
o
k
s
[
1
1
]
.
4
.
3
.
T
ra
ditio
na
l la
ng
ua
g
e
mo
dels
v
s
.
l
a
rg
e
la
ng
ua
g
e
m
o
d
els
Desp
ite
th
e
ef
f
ec
tiv
en
ess
o
f
tr
ad
itio
n
al
lan
g
u
a
g
e
m
o
d
els,
th
ey
s
till
ar
e
ch
allen
g
ed
b
y
m
o
r
e
co
m
p
lex
lan
g
u
ag
e
item
s
s
u
ch
as
id
io
m
s
,
m
etap
h
o
r
s
,
s
o
cial
co
n
tex
t,
an
d
im
p
licit
em
o
tio
n
s
.
Ho
we
v
er
,
L
L
Ms
s
h
o
w
a
m
ar
k
ed
in
cr
ea
s
e
in
n
atu
r
al
lan
g
u
ag
e
u
n
d
er
s
tan
d
in
g
(
NL
U)
.
T
h
ey
ar
e
tr
ain
ed
with
a
v
ar
ie
ty
o
f
tex
t
r
eso
u
r
ce
s
to
co
m
p
r
eh
en
d
th
e
b
ea
u
ty
a
n
d
co
m
p
le
x
ity
o
f
h
u
m
an
lan
g
u
ag
e.
T
ab
le
1
s
h
o
ws
h
o
w
e
f
f
ec
tiv
ely
L
L
Ms
im
p
r
o
v
e
p
er
f
o
r
m
an
ce
in
a
v
ar
iety
o
f
a
r
ea
s
,
b
u
t
with
ad
d
e
d
co
n
s
id
er
atio
n
f
o
r
a
p
p
licatio
n
r
elev
an
c
e.
Af
ter
ad
d
r
ess
in
g
th
e
lim
itatio
n
s
o
f
tr
a
d
itio
n
al
l
an
g
u
ag
e
m
o
d
els
ac
r
o
s
s
d
if
f
er
en
t
ap
p
licatio
n
d
o
m
ain
s
a
n
d
h
o
w
L
L
Ms
h
elp
ed
o
v
er
co
m
e
th
em
,
T
a
b
le
1
s
y
n
t
h
esizes th
e
d
is
cu
s
s
ed
id
ea
s
an
d
p
r
o
v
id
es a
n
o
v
e
r
v
iew
o
f
t
h
eir
in
ter
r
elatio
n
s
.
T
ab
le
1
.
T
h
e
lim
itatio
n
o
f
tr
ad
itio
n
al
lan
g
u
ag
e
ac
r
o
s
s
v
ar
io
u
s
d
im
en
s
io
n
s
an
d
L
L
M
e
n
h
an
ce
m
en
ts
,
a
d
o
m
ain
-
b
ased
co
m
p
ar
is
o
n
Tr
a
d
i
t
i
o
n
a
l
l
a
n
g
u
a
g
e
m
o
d
e
l
l
i
mi
t
a
t
i
o
n
LLM
a
ss
i
st
a
n
c
e
R
e
l
e
v
a
n
t
a
p
p
l
i
c
a
t
i
o
n
d
o
ma
i
n
Li
mi
t
a
t
i
o
n
1
:
p
e
r
f
o
r
mi
n
g
s
e
n
t
i
m
e
n
t
a
n
a
l
y
si
s
o
n
t
e
x
t
u
a
l
c
o
n
t
e
n
t
f
i
l
l
e
d
w
i
t
h
c
o
m
p
l
e
x
e
m
o
t
i
o
n
s
o
r
s
a
r
c
a
sm
i
s
c
h
a
l
l
e
n
g
i
n
g
.
LLM
s
b
r
i
d
g
e
d
t
h
i
s
g
a
p
a
n
d
h
e
l
p
e
d
m
a
c
h
i
n
e
s
w
i
t
h
i
d
e
n
t
i
f
y
i
n
g
a
n
d
i
n
t
e
r
p
r
e
t
i
n
g
l
a
y
e
r
e
d
sen
t
i
m
e
n
t
s,
e
n
su
r
i
n
g
h
i
g
h
p
r
e
c
i
si
o
n
.
M
a
r
k
e
t
s
e
a
r
c
h
[
2
0
]
,
s
o
c
i
a
l
me
d
i
a
a
n
a
l
y
t
i
c
s
[
2
1
]
,
a
n
d
c
u
s
t
o
mer
f
e
e
d
b
a
c
k
p
r
o
c
e
ssi
n
g
[
2
2
]
Li
mi
t
a
t
i
o
n
2
:
i
n
q
u
e
st
i
o
n
-
a
n
sw
e
r
i
n
g
sy
st
e
ms,
t
h
e
a
n
sw
e
r
i
s
g
e
n
e
r
a
t
e
d
a
c
c
o
r
d
i
n
g
t
o
k
e
y
w
o
r
d
a
ss
o
c
i
a
t
i
o
n
a
n
d
sema
n
t
i
c
r
e
a
s
o
n
i
n
g
.
LLM
s
i
n
q
u
e
st
i
o
n
a
n
sw
e
r
i
n
g
s
y
s
t
e
ms
a
l
l
o
w
t
h
e
m
t
o
c
a
p
t
u
r
e
t
h
e
c
o
r
e
o
f
i
n
q
u
i
r
i
e
s,
t
a
k
i
n
g
i
n
t
o
c
o
n
si
d
e
r
a
t
i
o
n
v
a
r
i
o
u
s
a
sp
e
c
t
s,
i
n
c
l
u
d
i
n
g
c
o
n
t
e
x
t
,
i
n
o
r
d
e
r
t
o
g
e
n
e
r
a
t
e
p
r
e
c
i
se
a
n
d
a
u
t
h
e
n
t
i
c
r
e
s
p
o
n
s
e
s.
C
u
s
t
o
mer
su
p
p
o
r
t
[
2
2
]
,
a
c
a
d
e
m
i
c
r
e
sea
r
c
h
[
2
3
]
,
a
n
d
i
n
t
e
l
l
i
g
e
n
t
t
u
t
o
r
i
n
g
s
y
st
e
ms
(
I
TS)
[
2
4
]
Li
mi
t
a
t
i
o
n
3
:
i
n
c
o
n
t
e
n
t
su
mm
a
r
i
z
a
t
i
o
n
,
t
r
a
d
i
t
i
o
n
a
l
LM
s
p
e
r
f
o
r
m
s
y
n
t
h
e
si
s
b
a
s
e
d
o
n
mo
d
e
s
t
k
e
y
se
n
t
e
n
c
e
s’
e
x
t
r
a
c
t
i
o
n
t
o
a
n
i
m
p
r
o
v
e
d
a
b
s
t
r
a
c
t
i
o
n
o
f
t
h
e
ma
i
n
i
d
e
a
.
LLM
s
h
a
v
e
t
h
e
a
b
i
l
i
t
y
t
o
p
r
o
c
e
ss
massi
v
e
c
o
r
p
o
r
a
,
u
n
d
e
r
s
t
a
n
d
t
h
e
d
o
m
i
n
a
n
t
t
h
e
m
e
s,
a
n
d
g
e
n
e
r
a
t
e
t
o
-
t
h
e
-
p
o
i
n
t
y
e
t
t
h
o
r
o
u
g
h
o
u
t
l
i
n
e
s
u
mm
a
r
i
e
s
.
A
c
a
d
e
mi
c
r
e
sea
r
c
h
[
2
3
]
a
n
d
b
u
s
i
n
e
ss
i
n
t
e
l
l
i
g
e
n
c
e
[
2
5
]
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.
15
,
No
.
3
,
J
u
n
e
20
26
:
2
1
4
1
-
2
1
5
2
2146
4
.
3
.
1
.
Sentim
ent
a
na
ly
s
is
W
h
ile
tr
ad
itio
n
al
lan
g
u
ag
e
m
o
d
els
ar
e
u
s
ed
f
o
r
s
en
tim
en
t
an
aly
s
is
,
th
er
e
ar
e
ce
r
tain
is
s
u
es
with
in
ter
p
r
etin
g
tex
tu
al
d
ata
i
n
m
o
r
e
c
o
m
p
lex
em
o
tio
n
c
o
n
tex
t
s
,
s
u
ch
as
s
ar
ca
s
m
[
2
6
]
.
L
L
Ms
ad
d
r
ess
th
is
is
s
u
e,
p
r
im
ar
ily
th
r
o
u
g
h
th
eir
ab
ilit
y
to
id
en
tify
lay
er
e
d
an
d
co
m
p
lex
s
en
tim
en
ts
.
T
h
is
is
v
alu
ab
le
in
m
an
y
f
ield
s
,
b
u
t th
e
m
o
s
t r
elev
an
t a
r
e
m
ar
k
et
r
esear
ch
,
s
o
cial
m
ed
ia
an
al
y
s
is
,
an
d
p
r
o
ce
s
s
in
g
cu
s
to
m
er
f
ee
d
b
ac
k
[
2
7
]
.
4
.
3
.
2
.
Q
ues
t
io
n a
ns
wer
ing
s
y
s
t
em
s
T
r
ad
itio
n
al
lan
g
u
a
g
e
m
o
d
els
ar
e
ap
p
lied
in
q
u
esti
o
n
-
a
n
s
wer
in
g
s
y
s
tem
s
.
T
h
e
an
s
wer
s
ar
e
g
en
er
ated
b
ased
o
n
g
iv
en
k
e
y
wo
r
d
ass
o
ciatio
n
an
d
s
em
an
tic
an
al
y
s
is
[
2
8
]
.
Me
an
wh
ile,
L
L
M
-
g
en
er
ated
an
s
wer
s
ar
e
b
u
ilt
u
p
o
n
ca
p
t
u
r
in
g
th
e
co
r
e
o
f
in
q
u
ir
ies.
L
L
Ms
tak
e
in
to
co
n
s
id
er
atio
n
th
e
co
n
te
x
t
o
f
a
g
iv
en
q
u
esti
o
n
t
o
cr
ea
te
ac
cu
r
ate
an
d
g
en
u
in
e
an
s
wer
s
.
Qu
esti
o
n
an
s
wer
in
g
s
y
s
tem
s
ar
e
ap
p
licab
le
ac
r
o
s
s
d
iv
er
s
e
d
o
m
ain
s
:
cu
s
to
m
er
s
u
p
p
o
r
t,
ac
ad
e
m
ic
s
ea
r
ch
,
an
d
I
T
S,
en
s
u
r
in
g
e
n
h
an
ce
m
en
ts
[
2
9
]
.
4
.
3
.
3
.
Co
nte
nt
s
um
m
a
riz
a
t
io
n
I
n
th
e
co
n
te
x
t
o
f
co
n
ten
t
s
u
m
m
ar
izatio
n
,
t
r
ad
itio
n
al
la
n
g
u
ag
e
m
o
d
els’
p
e
r
f
o
r
m
an
ce
is
wea
k
[
3
0
]
.
L
L
Ms
p
er
f
o
r
m
s
y
n
th
esis
b
ased
o
n
m
o
d
est
k
ey
s
en
ten
ce
ex
tr
ac
tio
n
to
g
en
e
r
ate
an
im
p
r
o
v
e
d
ab
s
tr
ac
tio
n
o
f
th
e
m
ain
id
ea
.
C
o
n
cu
r
r
en
tly
,
L
L
Ms
ca
n
p
r
o
ce
s
s
m
ass
iv
e
co
r
p
o
r
a
an
d
u
n
d
er
s
tan
d
th
e
d
o
m
in
a
n
t
th
em
es
[
3
1
]
.
T
h
e
y
ar
e
also
ca
p
ab
le
o
f
g
en
er
atin
g
to
-
th
e
-
p
o
in
t
y
et
th
o
r
o
u
g
h
o
u
t
lin
e
s
u
m
m
ar
ies.
R
elev
an
t
d
o
m
ain
s
th
at
ap
p
ly
to
s
u
ch
a
task
ar
e
ac
ad
em
ic
s
ea
r
c
h
,
n
ews a
g
g
r
e
g
atio
n
,
an
d
b
u
s
in
ess
in
tellig
en
ce
.
5.
L
ARG
E
L
ANG
UAG
E
M
O
D
E
L
:
CH
A
L
L
E
NG
E
S AN
D
L
I
M
I
T
A
T
I
O
N
S
T
h
o
u
g
h
L
L
Ms
p
r
esen
t
v
ar
io
u
s
p
o
s
itiv
e
asp
ec
ts
an
d
en
s
u
r
e
s
p
ec
if
ic
o
u
tco
m
es,
th
er
e
a
r
e
n
o
tab
l
e
lim
itatio
n
s
in
h
er
en
t
in
th
eir
s
tr
u
ctu
r
e.
On
e
n
o
ta
b
le
lim
itati
o
n
is
th
at
it
p
o
s
s
e
s
s
e
s
s
ig
n
if
ican
t
co
m
p
u
tatio
n
a
l
d
em
an
d
s
.
T
h
is
asp
ec
t
p
o
s
es
a
ch
allen
g
e
d
u
e
to
L
L
Ms
d
ep
en
d
en
c
y
o
n
lar
g
e
tr
ain
i
n
g
s
ets,
wh
ich
r
eq
u
ir
es
d
ev
elo
p
in
g
m
o
r
e
ef
f
icien
t
tr
ain
in
g
al
g
o
r
ith
m
s
.
So
lv
in
g
th
at
p
r
o
b
lem
will
p
r
o
tect
th
e
en
v
ir
o
n
m
en
t
an
d
r
ed
u
c
e
ca
r
b
o
n
f
o
o
t
p
r
in
ts
g
en
er
ate
d
in
cr
ea
tin
g
an
d
u
s
in
g
L
L
Ms
[
4
]
,
[
3
2
]
.
Ad
d
itio
n
ally
,
d
ata
p
r
iv
ac
y
an
d
s
en
s
itiv
ity
p
r
esen
t
a
n
o
th
e
r
c
o
n
s
tr
ain
t
o
n
th
e
ef
f
ec
tiv
e
n
ess
o
f
L
L
Ms.
T
h
is
lim
itatio
n
ar
is
es
f
r
o
m
L
L
Ms
tr
ain
in
g
o
n
v
ast
cr
o
s
s
-
s
ec
to
r
am
o
u
n
ts
o
f
d
ata,
wh
ich
s
till
r
ai
s
es
a
lo
t
o
f
co
n
ce
r
n
s
r
eg
a
r
d
in
g
u
n
in
ten
tio
n
al
d
is
clo
s
u
r
e
o
f
s
en
s
itiv
e
d
ata
b
y
th
ese
m
o
d
els
[
3
2
]
.
Mo
r
e
o
v
er
,
th
ese
m
o
d
els
ar
e
lim
ited
b
y
b
iases
in
g
r
ain
ed
in
th
e
d
ata
th
ey
wer
e
tr
ai
n
ed
o
n
.
T
h
is
f
ac
to
r
r
esu
lts
in
th
e
o
cc
u
r
r
en
ce
o
f
“h
allu
cin
atio
n
s
”,
a
s
itu
atio
n
wh
er
e
L
L
Ms
g
e
n
er
ate
a
r
es
p
o
n
s
e
th
at
is
d
ec
ep
tiv
ely
cr
e
d
ib
le
y
et
f
ac
tu
ally
in
co
r
r
ec
t.
W
h
ich
cr
ea
tes
th
e
p
o
ten
tial
o
f
b
u
ild
in
g
m
o
d
els
th
at
g
en
er
ate
m
is
lead
in
g
in
f
o
r
m
atio
n
an
d
“d
ee
p
f
ak
es”
[
4
]
,
[
3
2
]
.
An
o
th
er
lim
itatio
n
to
b
e
awa
r
e
o
f
c
o
r
r
elate
s
with
t
h
e
o
u
tco
m
es
r
esu
ltin
g
f
r
o
m
t
h
e
r
is
in
g
ad
o
p
tio
n
o
f
L
L
Ms
in
teg
r
atio
n
in
to
tec
h
n
ical
an
d
n
o
n
-
tech
n
ical
s
ec
to
r
s
.
No
o
n
e
d
en
ies
it
h
as
v
alu
ab
le
im
p
ac
t
o
n
au
to
m
atio
n
,
ef
f
icien
c
y
,
a
n
d
in
n
o
v
atio
n
.
Ho
we
v
er
,
it
h
as
p
r
o
f
o
u
n
d
ly
alter
ed
th
e
ec
o
n
o
m
ic
a
n
d
s
o
cial
lan
d
s
ca
p
e,
p
ar
ticu
lar
l
y
r
eg
a
r
d
i
n
g
jo
b
d
is
p
lace
m
en
t w
ith
in
th
e
wo
r
k
f
o
r
ce
[
3
2
]
.
Fu
r
th
er
m
o
r
e
,
th
e
m
o
d
el’
s
in
te
r
p
r
etab
ilit
y
in
tr
o
d
u
ce
s
a
s
ig
n
i
f
ican
t
co
n
s
id
er
atio
n
in
ap
p
ly
i
n
g
L
L
Ms.
Dee
p
lear
n
in
g
m
o
d
els
ar
e
k
n
o
wn
to
b
e
“b
lack
b
o
x
”
m
o
d
els
with
a
v
ast
n
u
m
b
e
r
o
f
p
ar
am
eter
s
;
h
en
ce
,
u
n
d
er
s
tan
d
i
n
g
an
d
an
s
wer
in
g
wh
y
th
e
m
o
d
el
o
p
te
d
f
o
r
a
s
p
ec
if
ic
d
ec
is
io
n
ca
n
b
e
h
ar
d
to
f
in
d
[
4
]
,
[
3
2
]
.
C
o
n
clu
d
in
g
th
e
lis
t,
t
h
e
s
ix
t
h
lim
itatio
n
is
r
elate
d
t
o
th
e
f
ac
t
th
at
L
L
Ms
lack
h
u
m
a
n
in
tu
itio
n
,
eth
ical
r
ea
s
o
n
in
g
,
an
d
ac
cu
m
u
latio
n
o
f
ex
p
er
ien
tial
k
n
o
wled
g
e.
Desp
ite
th
eir
ab
ilit
y
to
g
en
er
ate
r
esp
o
n
s
es
th
at
m
im
ic
h
u
m
a
n
ex
p
er
tis
e,
th
e
u
s
er
s
s
till
n
ee
d
to
ed
u
ca
te
th
e
m
s
elv
es
ab
o
u
t
th
e
m
o
d
el’
s
n
a
tu
r
e
to
a
d
ju
s
t
th
eir
lev
el
o
f
tr
u
s
two
r
th
i
n
ess
an
d
d
e
p
en
d
en
ce
o
n
its
o
u
t
p
u
t
[
4
]
,
[
3
2
]
.
6.
L
ARG
E
L
ANG
UAG
E
M
O
D
E
L
S IM
P
ACT O
N
E
DUCA
T
I
O
N
L
L
Ms
h
av
e
a
h
u
g
e
im
p
ac
t
o
n
d
iv
e
r
s
e
s
ec
to
r
s
;
ed
u
ca
tio
n
is
two
o
f
th
em
.
L
L
Ms
p
r
o
v
i
d
e
a
m
o
r
e
ad
ap
tiv
e,
a
p
p
ea
lin
g
,
an
d
e
f
f
icien
t
lear
n
in
g
en
v
ir
o
n
m
e
n
t
f
o
r
lear
n
er
s
f
r
o
m
d
if
f
e
r
en
t
b
ac
k
g
r
o
u
n
d
s
an
d
f
ield
s
[
3
3
]
.
L
L
Ms
h
av
e
b
ec
o
m
e
a
tr
an
s
f
o
r
m
ativ
e
f
o
r
ce
in
ed
u
ca
tio
n
,
d
r
iv
in
g
ad
v
an
ce
m
en
ts
ac
r
o
s
s
v
ar
io
u
s
e
d
u
ca
tio
n
al
f
u
n
ctio
n
alities
.
E
d
u
ca
tio
n
al
tech
n
o
lo
g
ies
ar
e
g
e
n
er
ally
ca
teg
o
r
iz
ed
in
to
f
o
u
r
m
ain
f
u
n
ctio
n
alities
[
3
4
]
.
I
n
t
h
is
p
a
r
t,
we
will
elab
o
r
ate
o
n
th
e
u
s
e
o
f
L
L
Ms
in
ea
ch
o
f
th
ese
f
u
n
ctio
n
alities
with
ex
am
p
les
f
r
o
m
r
ea
l
-
wo
r
ld
ap
p
licatio
n
s
.
T
ab
le
2
s
u
m
s
u
p
th
e
f
in
d
in
g
s
in
ter
m
s
o
f
im
p
le
m
en
tatio
n
o
f
L
L
Ms
ac
r
o
s
s
d
if
f
er
en
t e
d
u
ca
tio
n
al
f
u
n
ctio
n
alities
.
6
.
1
.
P
er
s
o
na
liza
t
io
n a
nd
a
da
ptiv
it
y
B
ein
g
o
n
e
o
f
t
h
e
m
o
s
t
im
p
o
r
ta
n
t
f
ea
t
u
r
es
i
n
c
u
r
r
e
n
t
e
-
l
ea
r
n
i
n
g
s
y
s
t
em
s
,
t
h
is
is
f
o
c
u
s
e
d
o
n
c
u
s
t
o
m
ize
d
ad
ap
tat
io
n
o
f
th
e
p
r
o
c
ess
o
f
le
ar
n
i
n
g
a
n
d
te
ac
h
i
n
g
b
a
s
ed
o
n
i
n
d
iv
id
u
al
s
t
u
d
e
n
ts
'
s
k
ills
as
we
ll
as
p
r
e
f
e
r
e
n
c
es
[
3
5
]
.
L
L
Ms
ca
n
b
e
v
e
r
y
h
el
p
f
u
l
i
n
d
o
in
g
t
h
is
b
y
o
f
f
e
r
i
n
g
g
u
i
d
e
d
lea
r
n
i
n
g
[
3
5
]
,
d
y
n
a
m
ic
all
y
Evaluation Warning : The document was created with Spire.PDF for Python.
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-
8
9
3
8
N
a
vig
a
tin
g
th
e
n
ew fr
o
n
tier
:
l
a
r
g
e
la
n
g
u
a
g
e
m
o
d
els a
n
d
th
e
ir
imp
lica
tio
n
s
fo
r
ed
u
ca
tio
n
(
La
ila
B
o
u
llo
u
s
)
2147
ad
j
u
s
t
in
g
co
n
t
e
n
t
t
o
t
h
e
p
r
o
g
r
ess
o
f
a
s
tu
d
e
n
t
[
1
5
]
,
[
3
3
]
,
[
3
6
]
.
C
r
e
ati
n
g
c
u
s
t
o
m
iz
e
d
c
o
n
te
n
t
ac
c
o
r
d
i
n
g
t
o
lea
r
n
i
n
g
s
t
y
l
es,
r
e
q
u
i
r
e
m
e
n
ts
,
a
n
d
p
e
r
f
o
r
m
a
n
c
e
i
n
f
o
r
m
ati
o
n
[
3
3
]
,
[
3
7
]
.
I
m
p
r
o
v
i
n
g
c
u
s
t
o
m
ize
d
l
ea
r
n
i
n
g
p
at
h
w
a
y
s
,
e
n
a
b
li
n
g
s
t
u
d
e
n
ts
t
o
p
r
o
ce
e
d
at
th
ei
r
p
ac
e
p
a
th
s
[
1
5
]
,
[
3
8
]
,
[
3
9
]
.
D
esi
g
n
i
n
g
ad
ap
ti
v
e
test
s
,
l
ik
e
co
m
p
u
te
r
-
b
as
e
d
m
u
lti
p
l
e
-
c
h
o
i
ce
q
u
es
ti
o
n
s
(
MCQs
)
,
i
n
d
ep
en
d
e
n
c
e
u
p
o
n
l
ea
r
n
e
r
p
r
o
f
il
es
a
n
d
in
f
o
r
m
a
ti
o
n
[
4
0
]
.
6
.
2
.
Ass
ess
m
ent
a
nd
ev
a
lua
t
io
n
L
L
Ms
p
r
o
v
id
e
v
a
r
io
u
s
m
ea
n
s
o
f
m
ak
in
g
th
e
ass
ess
m
en
t
p
r
o
ce
s
s
m
o
r
e
ef
f
icien
t
a
n
d
lig
h
ten
in
g
th
e
lo
ad
o
f
e
d
u
ca
to
r
s
.
So
m
e
ex
am
p
les
o
f
u
s
e
ar
e
s
m
ar
t
g
r
ad
in
g
,
wh
er
e
ess
ay
s
,
s
h
o
r
t
a
n
s
wer
s
,
o
r
e
v
en
co
d
e
ca
n
b
e
au
to
m
atica
lly
g
r
ad
e
d
f
o
r
tim
e
an
d
co
n
s
is
ten
cy
s
av
in
g
s
[
3
8
]
,
[
4
1
]
.
Au
to
m
ate
d
test
g
en
er
atio
n
,
lik
e
m
u
ltime
d
ia
-
s
u
p
p
o
r
ted
test
s
ta
ilo
r
ed
to
tar
g
et
s
p
ec
if
ic
lear
n
in
g
o
b
jectiv
es
[
2
4
]
,
[
4
2
]
.
Au
to
m
ated
f
ee
d
b
ac
k
g
en
er
atio
n
to
p
r
o
v
id
e
in
s
tan
t
,
co
n
s
tr
u
ctiv
e
f
ee
d
b
ac
k
t
o
s
tu
d
en
ts
[
2
4
]
,
[
4
3
]
,
[
4
4
]
.
Dis
cu
s
s
io
n
f
ac
ilit
atio
n
,
th
r
o
u
g
h
a
n
aly
s
is
an
d
e
n
g
ag
e
m
en
t
in
g
r
o
u
p
d
is
cu
s
s
io
n
s
,
to
allo
w
d
ee
p
er
p
ee
r
-
to
-
p
ee
r
in
ter
ac
tio
n
[
4
2
]
,
[
4
5
]
.
L
ast
b
u
t
n
o
t
least,
L
L
M
-
ass
i
s
ted
class
r
o
o
m
ass
is
tan
ce
,
in
wh
ich
m
o
d
els
ass
is
t
b
o
th
s
tu
d
en
ts
an
d
in
s
tr
u
cto
r
s
d
u
r
in
g
liv
e
s
ess
io
n
s
—
f
o
r
ex
am
p
le,
s
u
m
m
ar
izin
g
co
n
v
er
s
atio
n
s
,
an
s
wer
in
g
in
-
s
ess
io
n
q
u
er
ies,
o
r
g
iv
in
g
ex
p
lan
atio
n
s
o
n
d
em
a
n
d
[
4
6
]
.
6
.
3
.
P
r
o
f
ilin
g
a
nd
predict
ing
L
L
Ms
ca
n
an
aly
ze
s
tu
d
en
t
b
e
h
av
io
r
,
ac
tiv
ity
,
a
n
d
p
er
f
o
r
m
a
n
ce
s
o
as
to
ass
is
t
p
r
ed
ictiv
e
an
aly
tics
.
E
x
am
p
les
in
clu
d
e
i
d
en
tify
in
g
lev
els
o
f
co
m
p
r
eh
en
s
io
n
b
y
an
aly
zin
g
t
h
e
s
u
b
m
is
s
io
n
s
an
d
in
ter
ac
tio
n
s
o
f
s
tu
d
en
ts
[
3
]
,
[
4
7
]
.
Su
g
g
esti
n
g
lear
n
in
g
ac
tiv
ities
,
tailo
r
ed
to
cu
r
r
en
t
p
e
r
f
o
r
m
an
ce
[
3
]
.
Sen
tim
en
t
an
aly
s
is
,
s
o
th
at
lear
n
er
f
r
u
s
tr
atio
n
,
c
o
n
f
u
s
io
n
,
o
r
s
atis
f
ac
tio
n
ca
n
b
e
id
en
tifie
d
,
allo
win
g
th
e
in
s
t
r
u
cto
r
to
in
ter
v
e
n
e
p
r
ee
m
p
tiv
ely
[
3
6
]
,
[
4
3
]
.
6
.
4
.
I
nte
llig
ent
t
uto
ring
s
y
s
t
em
s
L
L
Ms
c
an
f
ac
i
lit
ate
I
T
S
t
o
im
p
le
m
e
n
t
m
o
r
e
a
d
v
a
n
c
ed
,
i
n
t
e
r
a
cti
v
e
f
ea
t
u
r
es
s
u
ch
as
o
n
e
-
on
-
o
n
e
v
i
r
t
u
a
l
tu
t
o
r
i
n
g
s
i
m
u
l
ati
n
g
h
u
m
a
n
-
li
k
e
d
i
al
o
g
u
e
tai
lo
r
e
d
t
o
t
h
e
s
t
u
d
e
n
t'
s
lea
r
n
i
n
g
r
eq
u
i
r
e
m
e
n
ts
[
3
]
,
[
3
9
]
,
[
4
8
]
,
[
4
9
]
.
Au
t
o
m
at
ed
q
u
est
io
n
-
a
n
d
-
an
s
wer
,
w
h
e
r
e
s
t
u
d
e
n
ts
m
a
y
as
k
f
o
l
lo
w
-
u
p
q
u
est
i
o
n
s
a
t
an
y
t
im
e
[
3
]
,
[
4
9
]
,
[
5
0
]
.
I
n
ter
ac
tiv
e
co
n
ten
t
g
e
n
er
atio
n
,
e.
g
.
,
lear
n
in
g
g
a
m
es,
s
ce
n
ar
io
s
,
an
d
s
im
u
latio
n
s
[
2
4
]
.
E
n
ab
lin
g
s
tu
d
en
t
d
is
cu
s
s
io
n
s
with
p
r
o
m
p
ts
o
r
in
tellig
en
t
ag
en
ts
to
g
u
id
e
th
e
d
is
cu
s
s
io
n
,
an
d
p
r
o
v
id
in
g
in
s
tan
t
f
ee
d
b
ac
k
,
r
ep
licatin
g
a
tu
to
r
'
s
p
r
o
m
p
tn
ess
d
u
r
in
g
lear
n
in
g
ex
e
r
cises
[
1
5
]
,
[
4
5
]
.
Desig
n
in
g
p
r
ac
ti
ce
ex
er
cises
,
e.
g
.
,
p
er
s
o
n
alize
d
q
u
izze
s
,
lo
g
ic
p
u
zz
les,
an
d
r
o
le
-
p
lay
in
g
s
ce
n
ar
io
s
[
1
5
]
,
[
4
5
]
,
[
4
9
]
,
[
5
1
]
.
Mim
ick
in
g
tu
to
r
-
lik
e
ac
to
r
s
,
e.
g
.
,
c
h
atb
o
t
-
b
ased
tu
to
r
s
lik
e
Nem
o
b
o
t,
wh
ic
h
p
r
o
v
id
e
co
n
s
tan
t su
p
p
o
r
t f
o
r
lear
n
in
g
[
5
2
]
.
T
ab
le
2
.
I
m
p
lem
en
tatio
n
o
f
L
L
M
ap
p
licatio
n
s
ac
r
o
s
s
m
ain
e
d
u
ca
tio
n
al
f
u
n
ctio
n
alities
Ed
u
c
a
t
i
o
n
a
l
f
u
n
c
t
i
o
n
a
l
i
t
y
LLM
a
p
p
l
i
c
a
t
i
o
n
P
e
r
so
n
a
l
i
z
a
t
i
o
n
a
n
d
a
d
a
p
t
i
v
i
t
y
–
G
u
i
d
e
d
r
e
a
d
i
n
g
[
3
5
]
–
C
o
n
t
e
n
t
g
e
n
e
r
a
t
i
o
n
[
1
5
]
,
[
3
3
]
–
A
u
t
o
ma
t
e
d
p
r
o
b
l
e
m ref
r
a
mi
n
g
a
c
r
o
ss
a
c
a
d
e
m
i
c
d
o
m
a
i
n
s
[
3
3
]
–
En
h
a
n
c
i
n
g
p
e
r
s
o
n
a
l
i
z
e
d
l
e
a
r
n
i
n
g
p
a
t
h
s
[
1
5
]
,
[
3
8
]
,
[
3
9
]
–
A
u
t
o
ma
t
e
d
c
r
e
a
t
i
o
n
o
f
M
C
Q
s
[
4
0
]
Ev
a
l
u
a
t
i
o
n
a
n
d
a
sse
ssm
e
n
t
–
S
mart
g
r
a
d
i
n
g
[
3
8
]
,
[
4
1
]
–
G
e
n
e
r
a
t
i
n
g
v
a
r
i
o
u
s res
o
u
r
c
e
s
,
i
n
c
l
u
d
i
n
g
a
ss
e
ssm
e
n
t
s
a
n
d
m
u
l
t
i
m
e
d
i
a
c
o
n
t
e
n
t
[
2
4
]
,
[
4
2
]
–
P
r
o
v
i
d
i
n
g
a
u
t
o
ma
t
e
d
,
p
r
o
c
e
ss
-
o
r
i
e
n
t
e
d
f
e
e
d
b
a
c
k
t
o
s
t
u
d
e
n
t
s
[
2
4
]
,
[
4
3
]
–
S
u
p
p
o
r
t
g
r
o
u
p
c
o
n
v
e
r
sa
t
i
o
n
s
[
4
2
]
,
[
4
5
]
–
a
u
t
o
m
a
t
e
d
c
r
e
a
t
i
o
n
o
f
M
C
Q
s
[
4
0
]
–
LLM
-
d
r
i
v
e
n
c
l
a
ssr
o
o
m fl
i
p
p
i
n
g
[
4
6
]
P
r
o
f
i
l
i
n
g
a
n
d
p
r
e
d
i
c
t
i
n
g
–
I
d
e
n
t
i
f
y
i
n
g
s
t
u
d
e
n
t
’
s
c
o
m
p
r
e
h
e
n
si
o
n
l
e
v
e
l
[
3
]
,
[
4
7
]
–
D
e
t
e
r
m
i
n
e
s
u
i
t
a
b
l
e
t
a
s
k
s fo
r
e
a
c
h
l
e
a
r
n
e
r
[
3
]
–
S
e
n
t
i
me
n
t
a
n
a
l
y
si
s
[
4
3
]
I
TS
–
P
r
o
v
i
d
i
n
g
o
n
e
-
on
-
o
n
e
t
u
t
o
r
i
n
g
f
o
r
st
u
d
e
n
t
s
[
3
]
,
[
3
9
]
–
A
n
sw
e
r
i
n
g
q
u
e
st
i
o
n
s
[
3
]
,
[
5
0
]
–
G
e
n
e
r
a
t
i
n
g
i
n
t
e
r
a
c
t
i
v
e
c
o
n
t
e
n
t
,
f
a
c
i
l
i
t
a
t
i
n
g
d
i
s
c
u
ss
i
o
n
s
,
a
n
d
p
r
o
v
i
d
i
n
g
r
e
a
l
-
t
i
me
f
e
e
d
b
a
c
k
[
2
4
]
–
R
e
a
l
-
w
o
r
l
d
p
r
a
c
t
i
c
e
t
h
r
o
u
g
h
g
e
n
e
r
a
t
i
n
g
p
e
r
s
o
n
a
l
i
z
e
d
p
u
z
z
l
e
s,
q
u
i
z
z
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,
a
n
d
r
o
l
e
-
p
l
a
y
i
n
g
sce
n
a
r
i
o
s
[
1
5
]
,
[
4
5
]
–
F
e
e
d
b
a
c
k
g
e
n
e
r
a
t
i
o
n
[
5
3
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i
mu
l
a
t
e
a
t
u
t
o
r
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l
i
k
e
e
x
p
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r
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i
t
h
a
c
h
a
t
b
o
t
l
i
k
e
N
e
mo
b
o
t
[
5
2
]
7.
CH
AL
L
E
NG
E
S AN
D
F
U
T
URE DI
RE
C
T
I
O
N
S
R
ec
en
t
s
tu
d
ies
h
av
e
s
h
o
wn
a
g
r
o
win
g
n
u
m
b
er
o
f
r
esear
ch
e
r
s
ex
p
lo
r
in
g
th
e
ap
p
licatio
n
o
f
L
L
Ms
in
ed
u
ca
tio
n
f
o
r
task
s
lik
e
ac
ad
e
m
ic
r
esear
ch
,
ass
ess
in
g
k
n
o
wled
g
e,
co
u
r
s
e
p
lan
n
in
g
,
an
d
m
o
r
e.
Ho
wev
er
,
m
a
n
y
ch
allen
g
es
an
d
o
p
p
o
r
tu
n
ities
r
em
ain
to
b
e
ad
d
r
ess
ed
.
Hav
in
g
ad
d
r
ess
ed
ea
ch
c
h
allen
g
e
s
e
p
ar
ately
,
it’s
u
s
ef
u
l
to
p
r
o
v
id
e
a
clea
r
er
v
iew.
Fig
u
r
e
4
s
u
m
m
ar
izes th
e
i
d
en
tifie
d
ch
allen
g
es.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
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tif
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tell
,
Vo
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15
,
No
.
3
,
J
u
n
e
20
26
:
2
1
4
1
-
2
1
5
2
2148
Fig
u
r
e
4
.
Key
ch
allen
g
es in
i
n
teg
r
atin
g
L
L
Ms w
ith
in
ed
u
ca
tio
n
al
co
n
te
x
ts
an
d
e
-
lear
n
in
g
s
y
s
tem
s
7
.
1
.
Student
m
o
delin
g
B
ef
o
r
e
th
e
ad
v
an
ce
m
en
t
o
f
L
L
Ms,
s
tu
d
en
t
b
eh
av
io
r
m
o
d
elin
g
in
th
e
d
ee
p
lear
n
in
g
e
r
a
r
elied
o
n
s
eq
u
en
tial
m
o
d
els
lik
e
R
NNs
an
d
tr
a
n
s
f
o
r
m
e
r
s
,
wh
ich
lac
k
ed
d
ir
ec
t
s
tu
d
e
n
t
f
ee
d
b
ac
k
an
d
o
f
ten
r
esu
lted
i
n
o
u
tco
m
es
th
at
wer
e
d
if
f
icu
lt
to
in
te
r
p
r
et.
T
h
e
in
tr
o
d
u
cti
o
n
o
f
L
L
M
-
b
ased
e
d
u
ca
tio
n
al
s
y
s
tem
s
en
ab
les
s
tu
d
en
ts
to
ex
p
r
ess
th
eir
n
ee
d
s
th
r
o
u
g
h
d
ialo
g
u
e,
allo
win
g
f
o
r
th
e
ex
tr
ac
tio
n
o
f
p
er
s
o
n
ali
ze
d
ch
ar
ac
ter
is
tics
s
u
ch
as
to
p
ic
m
aster
y
a
n
d
lea
r
n
in
g
p
r
ef
er
e
n
ce
s
.
Mo
r
e
o
v
er
,
L
L
Ms'
ab
ilit
ies
to
s
im
u
late
h
u
m
an
b
e
h
av
io
r
an
d
g
en
er
ate
h
u
m
a
n
-
lik
e
s
am
p
les
o
f
f
er
p
r
o
m
is
in
g
p
o
ten
tial
f
o
r
en
h
an
cin
g
s
tu
d
en
t
s
im
u
latio
n
s
.
Fo
r
s
tu
d
en
ts
with
lim
ited
in
ter
ac
tio
n
r
ec
o
r
d
s
,
L
L
M
-
b
ased
s
im
u
lato
r
s
ca
n
g
e
n
er
ate
ad
d
itio
n
al
d
ata
,
p
r
o
v
id
in
g
v
alu
ab
le
i
n
s
ig
h
ts
f
o
r
b
etter
u
n
d
e
r
s
tan
d
in
g
i
n
d
iv
i
d
u
al
lear
n
er
s
an
d
im
p
r
o
v
in
g
e
d
u
ca
to
r
s
'
teac
h
in
g
s
tr
ateg
ies
[5
1
]
.
7
.
2
.
Da
t
a
qu
a
lity
a
nd
priv
a
cy
Priv
ac
y
o
f
d
ata
an
d
p
r
iv
ac
y
o
f
p
er
s
o
n
al
in
f
o
r
m
atio
n
is
o
n
e
o
f
th
e
to
p
co
n
ce
r
n
s
with
u
s
in
g
L
L
Ms
b
ec
au
s
e
u
s
in
g
L
L
Ms
will
r
eq
u
ir
e
co
llectin
g
d
ata
o
n
lea
r
n
er
s
an
d
teac
h
e
r
s
p
er
s
o
n
al
a
n
d
lear
n
in
g
d
ata,
th
er
ef
o
r
e
cr
ea
tin
g
p
r
iv
ac
y
p
r
o
t
ec
tio
n
co
n
ce
r
n
s
ar
o
u
n
d
d
ata
c
o
llectio
n
.
Sch
o
o
ls
an
d
p
r
o
g
r
a
m
s
m
u
s
t p
r
o
tect
an
d
en
s
u
r
e
th
e
p
r
i
v
ac
y
o
f
th
eir
s
tu
d
en
ts
/lear
n
er
s
an
d
e
d
u
ca
to
r
s
wh
ile
p
r
o
tectin
g
a
n
d
s
ec
u
r
i
n
g
th
e
d
ata
th
at
is
co
llected
.
Par
en
ts
an
d
ed
u
ca
to
r
s
ar
e
also
cr
itical
f
o
r
h
elp
in
g
ed
u
ca
te
ch
ild
r
en
o
n
d
ata
p
r
i
v
ac
y
an
d
s
af
ety
an
d
m
an
ag
in
g
r
is
k
s
wh
en
u
s
in
g
L
L
Ms.
I
n
r
eg
ar
d
to
th
e
c
o
llectio
n
an
d
u
s
e
o
f
s
tu
d
e
n
t
lear
n
in
g
d
ata
th
er
e
h
av
e
t
o
b
e
ap
p
r
o
p
r
iate
p
r
i
v
ac
y
p
r
o
tectio
n
s
,
th
is
will
h
elp
en
s
u
r
e
th
at
s
tu
d
en
t
lear
n
in
g
d
ata
is
n
o
t
h
ac
k
ed
an
d
th
at
d
ata
is
n
o
t
u
s
ed
f
o
r
u
n
i
n
ten
d
ed
o
u
tco
m
es.
As
th
e
wo
r
ld
is
en
ter
in
g
an
u
n
u
s
u
al
p
er
io
d
o
f
i
n
tellig
en
ce
-
ass
is
ted
ed
u
ca
tio
n
,
s
ee
n
th
r
o
u
g
h
L
L
M
m
o
d
els
lik
e
C
h
atGPT
,
G
PT
-
4
,
an
d
Ma
th
GPT,
L
L
Ms
will
tak
e
ed
u
ca
tio
n
b
ey
o
n
d
lim
itatio
n
s
an
d
we
n
ee
d
to
co
n
tin
u
e
to
f
in
d
m
eth
o
d
s
to
"tr
ain
"
L
L
Ms
m
o
r
e
ef
f
ec
tiv
ely
an
d
m
ak
e
im
p
r
o
v
em
e
n
ts
to
lar
g
e
-
s
ca
le
m
o
d
els as a
wh
o
le
[5
4
]
.
7
.
3
.
T
ec
hn
ica
l c
o
m
plex
it
y
L
L
Ms
ar
e
b
ec
o
m
in
g
in
cr
ea
s
in
g
ly
c
o
m
p
lex
,
with
c
o
m
p
u
t
atio
n
tim
es
f
o
r
s
in
g
le
-
s
tep
p
r
o
ce
s
s
es
in
cr
ea
s
in
g
m
o
r
e
th
an
ten
f
o
ld
.
T
ask
s
th
at
o
n
ce
to
o
k
a
f
ew
h
o
u
r
s
to
tr
ain
n
o
w
tak
e
s
ev
er
al
d
ay
s
,
with
th
e
n
ee
d
to
m
ain
tain
a
o
n
e
-
d
a
y
lim
it
f
o
r
test
in
g
s
till
ess
en
tial.
T
h
e
co
s
t
o
f
tr
ain
in
g
lar
g
e,
g
en
er
al
-
p
u
r
p
o
s
e
m
o
d
els
is
s
ig
n
if
ican
t
an
d
escalate
s
wh
e
n
o
p
tim
izatio
n
,
u
p
d
ates,
an
d
d
ep
lo
y
m
e
n
t
ar
e
i
n
clu
d
ed
.
I
n
t
eg
r
atin
g
L
L
Ms
in
to
ex
is
tin
g
ed
u
ca
tio
n
s
y
s
tem
s
p
r
esen
ts
s
ig
n
if
ican
t
tech
n
ical
ch
allen
g
es.
Sch
o
o
ls
m
a
y
s
tr
u
g
g
le
to
alig
n
L
L
M
ca
p
ab
ilit
ies with
th
eir
cu
r
r
en
t I
T
in
f
r
astru
ctu
r
es,
wh
ich
m
ay
r
eq
u
ir
e
s
ig
n
if
ican
t u
p
g
r
ad
es a
n
d
cu
s
to
m
izatio
n
to
s
u
p
p
o
r
t
t
h
ese
ad
v
a
n
ce
d
tec
h
n
o
lo
g
ies.
I
n
ad
d
itio
n
,
C
h
atGPT
'
s
r
elian
ce
o
n
a
lar
g
e
n
u
m
b
er
o
f
GPU
ch
ip
s
f
o
r
d
ata
p
r
o
ce
s
s
in
g
ad
d
s
f
u
r
th
er
c
o
m
p
lex
ity
a
n
d
co
s
t to
d
ep
lo
y
m
en
t
[5
4
]
.
7
.
4
.
E
ns
uring
a
ca
dem
ic
inte
g
rit
y
L
L
Ms
p
r
o
d
u
ce
tex
t
th
at
ca
n
r
iv
al
o
r
ev
en
s
u
r
p
ass
h
u
m
an
wr
itin
g
in
ter
m
s
o
f
f
lu
en
cy
an
d
q
u
ality
.
W
h
ile
th
is
ar
ticle
h
ig
h
lig
h
ts
th
eir
p
o
ten
tial
in
ed
u
ca
tio
n
al
co
n
tex
ts
,
it
is
im
p
o
r
ta
n
t
to
r
ec
o
g
n
ize
th
at
o
v
er
-
r
elian
ce
o
n
L
L
Ms
ca
n
in
h
ib
it
n
atu
r
al
lear
n
in
g
p
r
o
ce
s
s
es.
Usi
n
g
L
L
Ms
f
o
r
h
o
m
ew
o
r
k
h
elp
is
ac
ce
p
tab
le,
b
u
t
r
ely
in
g
o
n
th
em
to
co
m
p
lete
task
s
ca
n
lim
it
o
p
p
o
r
tu
n
i
ties
f
o
r
m
ea
n
in
g
f
u
l
p
r
ac
tice
an
d
u
n
d
e
r
s
tan
d
in
g
.
Dete
ctin
g
L
L
M
-
g
en
e
r
ated
c
o
n
ten
t
is
ess
en
tial
to
p
r
ev
e
n
t
ch
e
atin
g
an
d
m
ain
tain
ed
u
ca
tio
n
a
l
in
teg
r
ity
.
R
ec
en
t
s
tu
d
ies
h
av
e
i
n
tr
o
d
u
ce
d
two
m
ain
d
etec
tio
n
s
tr
ateg
ies:
s
tatis
tical
o
u
tlier
d
etec
tio
n
,
wh
ich
id
en
tifie
s
v
ar
iatio
n
s
in
lin
g
u
is
tic
f
ea
tu
r
es,
an
d
s
u
p
er
v
is
ed
class
if
ier
s
,
wh
ich
u
s
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
tr
ain
ed
o
n
ex
am
p
les
o
f
b
o
th
h
u
m
a
n
a
n
d
L
L
M
-
g
en
er
ated
tex
ts
.
As
L
L
Ms
ev
o
lv
e
,
d
etec
tio
n
m
eth
o
d
s
m
u
s
t
also
e
v
o
lv
e,
in
co
r
p
o
r
atin
g
s
tatis
tical
an
aly
s
is
,
m
ac
h
in
e
lear
n
in
g
,
a
n
d
p
o
t
en
tially
n
ew
tech
n
iq
u
es
to
e
f
f
ec
tiv
ely
d
is
tin
g
u
is
h
L
L
M
o
u
tp
u
t
f
r
o
m
h
u
m
a
n
-
wr
it
ten
co
n
ten
t
[5
1
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
N
a
vig
a
tin
g
th
e
n
ew fr
o
n
tier
:
l
a
r
g
e
la
n
g
u
a
g
e
m
o
d
els a
n
d
th
e
ir
imp
lica
tio
n
s
fo
r
ed
u
ca
tio
n
(
La
ila
B
o
u
llo
u
s
)
2149
7
.
5
.
M
ulti
-
m
o
da
l inf
o
r
m
a
t
io
n in e
du
ca
t
io
n
Mu
ltimo
d
al
in
f
o
r
m
atio
n
in
e
d
u
ca
tio
n
is
n
o
t
a
n
o
v
el
p
h
e
n
o
m
en
o
n
.
Fo
r
ex
am
p
le,
g
e
o
m
etr
ic
wo
r
d
p
r
o
b
lem
s
i
n
clu
d
e
im
ag
es
alo
n
g
with
tex
t
a
n
d
tex
tb
o
o
k
s
o
f
ten
u
tili
ze
tex
t
in
c
o
n
ju
n
ctio
n
with
im
ag
es
f
o
r
a
co
n
ce
p
t
to
o
.
T
h
is
r
ep
r
esen
tati
o
n
an
d
m
u
ltimo
d
al
alig
n
m
en
t
ar
e
p
a
r
t
o
f
wh
at
m
ak
es
an
in
tellig
en
t
ed
u
ca
tio
n
s
y
s
tem
o
p
tim
al,
an
d
u
ltima
tely
wh
at
m
ak
es
a
m
u
ltimo
d
al
i
n
f
o
r
m
atio
n
s
y
s
tem
m
o
r
e
r
o
b
u
s
t.
T
h
e
m
u
ltimo
d
al
lan
d
s
ca
p
e
is
r
ap
id
ly
e
v
o
lv
in
g
,
esp
ec
ially
f
o
r
L
L
Ms,
as
v
ar
i
o
u
s
ar
ch
itectu
r
es
an
d
p
r
etr
ai
n
in
g
task
s
ar
e
b
ein
g
estab
lis
h
ed
as
well.
Ho
wev
er
,
ed
u
ca
tio
n
al
co
n
tex
ts
r
ep
r
e
s
en
t
a
u
n
iq
u
e
an
d
co
m
p
lex
ca
s
e
to
d
ea
l
with
m
u
ltimo
d
al
in
f
o
r
m
atio
n
.
I
m
ag
es
an
d
tex
t
in
th
e
ed
u
ca
tio
n
al
co
n
tex
t
ar
e
o
f
te
n
v
er
y
p
r
ec
is
ely
alig
n
ed
with
o
n
e
an
o
th
er
.
Fo
r
ex
a
m
p
le,
g
eo
m
et
r
y
wo
r
d
p
r
o
b
lem
s
ca
n
h
av
e
v
er
y
s
p
ec
if
ic
in
f
o
r
m
atio
n
ab
o
u
t
th
e
p
ar
am
eter
s
o
f
th
e
g
eo
m
etr
ic
s
h
ap
es
in
th
e
im
ag
es
as
well
a
s
ae
s
th
eti
c
d
etails,
th
at
r
eq
u
ir
e
eq
u
ally
p
r
ec
is
e
v
er
b
al
r
ep
r
esen
tatio
n
as
well.
T
h
e
m
o
d
el
h
as
to
b
e
ab
le
to
r
ep
r
esen
t
th
e
im
ag
e
all
th
e
in
f
o
r
m
atio
n
an
d
d
etails
co
n
v
ey
e
d
in
th
e
v
is
u
al
m
o
d
a
lity
.
C
r
o
s
s
-
m
o
d
el
r
ea
s
o
n
in
g
i
s
ty
p
ically
r
eq
u
ir
ed
f
o
r
e
d
u
c
atio
n
al
m
u
ltimo
d
al
in
f
o
r
m
atio
n
r
ep
r
esen
tatio
n
an
d
d
atasets
d
o
n
o
t
u
s
u
ally
r
ep
r
e
s
en
t c
r
o
s
s
-
m
o
d
al
r
ea
s
o
n
in
g
co
m
p
lex
ities
.
8.
CO
NCLU
SI
O
N
T
h
i
s
r
ev
i
ew
g
o
e
s
b
ey
o
n
d
h
i
g
h
-
lev
el
p
r
o
m
i
s
e
s
an
d
m
ak
e
s
s
p
ec
i
f
i
c
s
t
at
em
en
t
s
o
f
h
o
w
L
L
M
s
ca
n
b
e
a
f
o
r
ce
f
o
r
b
e
tte
r
e
-
l
ea
r
n
in
g
,
an
d
wh
a
t
n
ee
d
s
to
b
e
m
an
a
g
ed
,
in
o
r
d
er
to
d
o
s
o
r
e
s
p
o
n
s
ib
ly
.
Fir
s
t,
o
u
r
ed
u
ca
t
io
n
-
f
o
cu
s
ed
tax
o
n
o
m
y
m
ak
e
s
an
e
x
p
l
ic
it
co
n
n
ec
tio
n
b
et
wee
n
L
L
M
ca
p
ab
i
li
t
ie
s
an
d
f
o
u
r
p
ed
ag
o
g
ic
al
f
u
n
ct
io
n
s
,
wh
ich
ca
n
u
s
ef
u
lly
in
f
o
r
m
s
e
lec
t
io
n
an
d
e
v
alu
at
iv
e
ef
f
o
r
t
s
.
Se
co
n
d
,
o
u
r
d
o
m
a
in
-
b
as
ed
co
m
p
ar
i
s
o
n
cla
r
if
ie
s
wh
ich
s
p
ec
i
f
i
c
ar
ea
s
L
L
M
s
h
av
e
u
n
am
b
ig
u
o
u
s
ad
v
an
tag
es
o
v
er
tr
a
d
i
tio
n
al
p
ed
ag
o
g
i
ca
l
m
o
d
el
s
(
i.
e.
,
n
u
an
c
ed
s
en
tim
en
t,
co
n
t
ex
tu
a
l
q
u
e
s
tio
n
an
s
wer
in
g
,
an
d
tr
u
e
ab
s
tr
a
ct
iv
e
s
u
m
m
ar
iza
t
io
n
)
an
d
h
o
w
th
a
t
m
ig
h
t
m
e
an
b
et
ter
f
ee
d
b
ac
k
,
g
u
id
an
ce
,
o
r
s
tu
d
y
s
u
p
p
o
r
t
f
o
r
le
ar
n
er
s
.
T
h
ir
d
,
w
e
ag
g
r
eg
a
te
s
ix
s
y
s
t
em
ic
t
h
r
e
at
s
t
o
th
eir
u
s
e
(
co
m
p
u
ta
tio
n
a
l
co
s
t
/c
ar
b
o
n
,
p
r
iv
ac
y
a
n
d
d
a
ta
g
o
v
e
r
n
an
c
e,
b
i
a
s
an
d
h
al
lu
c
in
a
tio
n
,
wo
r
k
f
o
r
ce
ch
a
ll
en
g
e
s
,
m
o
d
e
l
o
p
a
ci
ty
,
an
d
li
m
it
s
o
f
h
u
m
an
j
u
d
g
m
en
t)
a
s
d
es
ig
n
o
p
p
o
r
tu
n
i
ti
es
(
to
d
e
s
ig
n
f
o
r
p
r
iv
a
cy
-
by
-
d
es
ig
n
d
a
ta
m
an
ag
em
en
t
,
b
ia
s
a
u
d
it
s
,
v
er
if
iab
le
g
r
o
u
n
d
in
g
,
e
d
u
ca
to
r
-
in
-
th
e
-
lo
o
p
o
v
er
s
ig
h
t,
an
d
ex
p
la
in
ab
i
li
t
y
f
o
r
p
ed
ag
o
g
i
ca
l
p
r
ac
ti
ce
s
)
.
F
in
a
lly
,
o
u
r
r
ef
l
ex
iv
e
m
eth
o
d
o
lo
g
y
f
o
r
d
o
cu
m
en
t
in
g
o
u
r
r
ev
i
ew
p
r
o
ce
s
s
an
d
cu
r
a
ted
b
o
d
y
o
f
d
o
cu
m
en
t
s
cr
ea
te
s
a
li
v
in
g
r
e
s
o
u
r
ce
f
o
r
r
e
ca
p
s
,
o
r
p
r
ep
ar
in
g
f
o
r
ad
v
an
c
e
s
in
m
o
r
e
s
u
b
s
t
an
t
iv
e
m
o
d
e
l d
ev
e
lo
p
m
en
t,
p
o
l
icy
r
eg
u
la
t
io
n
s
,
o
r
c
la
s
s
r
o
o
m
p
r
a
ct
ic
e.
ACK
NO
WL
E
DG
M
E
N
T
S
T
h
e
au
t
h
o
r
s
wo
u
ld
lik
e
to
t
h
an
k
th
e
Ar
tific
ial
I
n
tellig
en
ce
an
d
C
o
m
p
lex
Sy
s
tem
s
E
n
g
in
ee
r
in
g
L
ab
o
r
ato
r
y
(
E
NSAM,
Hass
an
I
I
Un
iv
e
r
s
ity
)
an
d
th
e
Sm
ar
til
ab
L
ab
o
r
ato
r
y
(
M
o
r
o
cc
a
n
Sch
o
o
l
o
f
E
n
g
in
ee
r
i
n
g
)
f
o
r
th
eir
s
u
p
p
o
r
t.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
r
esear
ch
was
s
u
p
p
o
r
ted
b
y
th
e
Ar
tific
ial
I
n
tellig
en
ce
an
d
C
o
m
p
lex
Sy
s
tem
s
E
n
g
in
ee
r
in
g
L
ab
o
r
ato
r
y
(
E
NSAM,
Hass
an
I
I
Un
iv
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r
s
ity
,
C
asab
lan
ca
,
Mo
r
o
cc
o
)
an
d
L
a
b
o
r
at
o
r
y
Sm
ar
tilab
(
Mo
r
o
cc
an
Sch
o
o
l o
f
E
n
g
in
ee
r
in
g
Scien
c
es,
R
ab
at,
Mo
r
o
cc
o
)
AUTHO
R
CO
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B
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h
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C
o
n
tr
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u
to
r
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o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
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o
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ize
in
d
iv
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al
au
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o
r
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as
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eq
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r
o
m
in
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E
T
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CAL AP
P
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p
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.
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