Journal of Medical Internet Research,
Год журнала:
2024,
Номер
26, С. e56780 - e56780
Опубликована: Май 31, 2024
Large
language
models
(LLMs)
such
as
ChatGPT
have
become
widely
applied
in
the
field
of
medical
research.
In
process
conducting
systematic
reviews,
similar
tools
can
be
used
to
expedite
various
steps,
including
defining
clinical
questions,
performing
literature
search,
document
screening,
information
extraction,
and
refinement,
thereby
conserving
resources
enhancing
efficiency.
However,
when
using
LLMs,
attention
should
paid
transparent
reporting,
distinguishing
between
genuine
false
content,
avoiding
academic
misconduct.
this
viewpoint,
we
highlight
potential
roles
LLMs
creation
reviews
meta-analyses,
elucidating
their
advantages,
limitations,
future
research
directions,
aiming
provide
insights
guidance
for
authors
planning
meta-analyses.
Frontiers of Computer Science,
Год журнала:
2024,
Номер
18(6)
Опубликована: Ноя. 11, 2024
Abstract
Information
Extraction
(IE)
aims
to
extract
structural
knowledge
from
plain
natural
language
texts.
Recently,
generative
Large
Language
Models
(LLMs)
have
demonstrated
remarkable
capabilities
in
text
understanding
and
generation.
As
a
result,
numerous
works
been
proposed
integrate
LLMs
for
IE
tasks
based
on
paradigm.
To
conduct
comprehensive
systematic
review
exploration
of
LLM
efforts
tasks,
this
study,
we
survey
the
most
recent
advancements
field.
We
first
present
an
extensive
overview
by
categorizing
these
terms
various
subtasks
techniques,
then
empirically
analyze
advanced
methods
discover
emerging
trend
with
LLMs.
Based
thorough
conducted,
identify
several
insights
technique
promising
research
directions
that
deserve
further
future
studies.
maintain
public
repository
consistently
update
related
resources
GitHub
(LLM4IE
repository).
ACM transactions on office information systems,
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 13, 2024
With
the
rapid
development
of
online
services
and
web
applications,
recommender
systems
(RS)
have
become
increasingly
indispensable
for
mitigating
information
overload
matching
users’
needs
by
providing
personalized
suggestions
over
items.
Although
RS
research
community
has
made
remarkable
progress
past
decades,
conventional
recommendation
models
(CRM)
still
some
limitations,
e.g.
,
lacking
open-domain
world
knowledge,
difficulties
in
comprehending
underlying
preferences
motivations.
Meanwhile,
large
language
(LLM)
shown
impressive
general
intelligence
human-like
capabilities
various
natural
processing
(NLP)
tasks,
which
mainly
stem
from
their
extensive
open-world
logical
commonsense
reasoning
abilities,
as
well
comprehension
human
culture
society.
Consequently,
emergence
LLM
is
inspiring
design
pointing
out
a
promising
direction,
i.e.
whether
we
can
incorporate
benefit
common
knowledge
to
compensate
limitations
CRM.
In
this
paper,
conduct
comprehensive
survey
on
draw
bird’s-eye
view
perspective
whole
pipeline
real-world
systems.
Specifically,
summarize
existing
works
two
orthogonal
aspects:
where
how
adapt
RS.
For
“
WHERE
”
question,
discuss
roles
that
could
play
different
stages
pipeline,
feature
engineering,
encoder,
scoring/ranking
function,
user
interaction,
controller.
HOW
investigate
training
inference
strategies,
resulting
fine-grained
taxonomy
criteria,
tune
or
not
during
training,
involve
inference.
Detailed
analysis
paths
are
provided
both
“WHERE”
“HOW”
questions,
respectively.
Then,
highlight
key
challenges
adapting
three
aspects,
efficiency,
effectiveness,
ethics.
Finally,
future
prospects.
To
further
facilitate
LLM-enhanced
systems,
actively
maintain
GitHub
repository
papers
other
related
resources
rising
direction
1
.
Cell,
Год журнала:
2024,
Номер
187(22), С. 6125 - 6151
Опубликована: Окт. 1, 2024
We
envision
"AI
scientists"
as
systems
capable
of
skeptical
learning
and
reasoning
that
empower
biomedical
research
through
collaborative
agents
integrate
AI
models
tools
with
experimental
platforms.
Rather
than
taking
humans
out
the
discovery
process,
combine
human
creativity
expertise
AI's
ability
to
analyze
large
datasets,
navigate
hypothesis
spaces,
execute
repetitive
tasks.
are
poised
be
proficient
in
various
tasks,
planning
workflows
performing
self-assessment
identify
mitigate
gaps
their
knowledge.
These
use
language
generative
feature
structured
memory
for
continual
machine
incorporate
scientific
knowledge,
biological
principles,
theories.
can
impact
areas
ranging
from
virtual
cell
simulation,
programmable
control
phenotypes,
design
cellular
circuits
developing
new
therapies.
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval,
Год журнала:
2024,
Номер
unknown, С. 1104 - 1114
In
this
paper,
we
introduce
the
design
and
evaluation
of
an
LLM-based
AI
agent
for
human-agent
interaction
in
Virtual
Reality
(VR).
Our
system
leverages
GPT-4,
a
Large
Language
Model
(LLM)
to
simulate
human
behavior.
agent,
deployed
VRChat
as
Non-playable
Character
(NPC),
exhibits
ability
respond
player
by
providing
context-relevant
responses
followed
appropriate
facial
expressions
body
gestures.
preliminary
yielded
most
optimal
parameters
generating
plausible
responses.
With
our
system,
lay
groundwork
future
development
NPCs
VR.
Digital Discovery,
Год журнала:
2024,
Номер
3(7), С. 1389 - 1409
Опубликована: Янв. 1, 2024
ProtAgents
is
a
de
novo
protein
design
platform
based
on
multimodal
LLMs,
where
distinct
AI
agents
with
expertise
in
knowledge
retrieval,
structure
analysis,
physics-based
simulations,
and
results
analysis
tackle
tasks
dynamic
setting.
