Communications Psychology,
Journal Year:
2024,
Volume and Issue:
2(1)
Published: June 3, 2024
In
the
present
study,
we
investigate
and
compare
reasoning
in
large
language
models
(LLMs)
humans,
using
a
selection
of
cognitive
psychology
tools
traditionally
dedicated
to
study
(bounded)
rationality.
We
presented
human
participants
an
array
pretrained
LLMs
new
variants
classical
experiments,
cross-compared
their
performances.
Our
results
showed
that
most
included
errors
akin
those
frequently
ascribed
error-prone,
heuristic-based
reasoning.
Notwithstanding
this
superficial
similarity,
in-depth
comparison
between
humans
indicated
important
differences
with
human-like
reasoning,
models'
limitations
disappearing
almost
entirely
more
recent
LLMs'
releases.
Moreover,
show
while
it
is
possible
devise
strategies
induce
better
performance,
machines
are
not
equally
responsive
same
prompting
schemes.
conclude
by
discussing
epistemological
implications
challenges
comparing
machine
behavior
for
both
artificial
intelligence
psychology.
Current Oncology,
Journal Year:
2024,
Volume and Issue:
31(4), P. 1817 - 1830
Published: March 29, 2024
The
technological
capability
of
artificial
intelligence
(AI)
continues
to
advance
with
great
strength.
Recently,
the
release
large
language
models
has
taken
world
by
storm
concurrent
excitement
and
concern.
As
a
consequence
their
impressive
ability
versatility,
provide
potential
opportunity
for
implementation
in
oncology.
Areas
possible
application
include
supporting
clinical
decision
making,
education,
contributing
cancer
research.
Despite
promises
that
these
novel
systems
can
offer,
several
limitations
barriers
challenge
implementation.
It
is
imperative
concerns,
such
as
accountability,
data
inaccuracy,
protection,
are
addressed
prior
integration
progression
continues,
new
ethical
practical
dilemmas
will
also
be
approached;
thus,
evaluation
concerns
dynamic
nature.
This
review
offers
comprehensive
overview
oncology,
well
surrounding
care.
Behavior Research Methods,
Journal Year:
2024,
Volume and Issue:
56(8), P. 8214 - 8237
Published: Aug. 15, 2024
Large
language
models
(LLMs)
have
the
potential
to
revolutionize
behavioral
science
by
accelerating
and
improving
research
cycle,
from
conceptualization
data
analysis.
Unlike
closed-source
solutions,
open-source
frameworks
for
LLMs
can
enable
transparency,
reproducibility,
adherence
protection
standards,
which
gives
them
a
crucial
advantage
use
in
science.
To
help
researchers
harness
promise
of
LLMs,
this
tutorial
offers
primer
on
Hugging
Face
ecosystem
demonstrates
several
applications
that
advance
conceptual
empirical
work
science,
including
feature
extraction,
fine-tuning
prediction,
generation
responses.
Executable
code
is
made
available
at
github.com/Zak-Hussain/LLM4BeSci.git
.
Finally,
discusses
challenges
faced
with
(open-source)
related
interpretability
safety
perspective
future
intersection
modeling
International Journal of Selection and Assessment,
Journal Year:
2024,
Volume and Issue:
32(4), P. 499 - 511
Published: May 17, 2024
Abstract
Unproctored
assessments
are
widely
used
in
pre‐employment
assessment.
However,
accessible
large
language
models
(LLMs)
pose
challenges
for
unproctored
personnel
assessments,
given
that
applicants
may
use
them
to
artificially
inflate
their
scores
beyond
true
abilities.
This
be
particularly
concerning
cognitive
ability
tests,
which
and
traditionally
considered
less
fakeable
by
humans
than
personality
tests.
Thus,
this
study
compares
the
performance
of
LLMs
on
two
common
types
tests:
quantitative
(number
series
completion)
verbal
(use
a
passage
text
determine
whether
statement
is
true).
The
tests
investigated
real‐world,
high‐stakes
selection.
We
also
examine
across
different
test
formats
(i.e.,
open‐ended
vs.
multiple
choice).
Further,
we
contrast
(Generative
Pretrained
Transformers,
GPT‐3.5
GPT‐4)
prompt
approaches
“temperature”
settings
parameter
determines
amount
randomness
model's
output).
found
performed
well
but
extremely
poorly
test,
even
when
accounting
format.
GPT‐4
outperformed
both
Notably,
although
temperature
did
affect
LLM
performance,
those
effects
were
mostly
minor
relative
differences
models.
provide
recommendations
securing
testing
against
influences.
Additionally,
call
rigorous
research
investigating
prevalence
usage
as
how
affects
selection
validity.
Communications Psychology,
Journal Year:
2024,
Volume and Issue:
2(1)
Published: June 3, 2024
In
the
present
study,
we
investigate
and
compare
reasoning
in
large
language
models
(LLMs)
humans,
using
a
selection
of
cognitive
psychology
tools
traditionally
dedicated
to
study
(bounded)
rationality.
We
presented
human
participants
an
array
pretrained
LLMs
new
variants
classical
experiments,
cross-compared
their
performances.
Our
results
showed
that
most
included
errors
akin
those
frequently
ascribed
error-prone,
heuristic-based
reasoning.
Notwithstanding
this
superficial
similarity,
in-depth
comparison
between
humans
indicated
important
differences
with
human-like
reasoning,
models'
limitations
disappearing
almost
entirely
more
recent
LLMs'
releases.
Moreover,
show
while
it
is
possible
devise
strategies
induce
better
performance,
machines
are
not
equally
responsive
same
prompting
schemes.
conclude
by
discussing
epistemological
implications
challenges
comparing
machine
behavior
for
both
artificial
intelligence
psychology.
