Who Will Author the Synthetic Texts? Evoking Multiple Personas from Large Language Models to Represent Users’ Associative Thesauri
Big Data and Cognitive Computing,
Journal Year:
2025,
Volume and Issue:
9(2), P. 46 - 46
Published: Feb. 18, 2025
Previously,
it
was
suggested
that
the
“persona-driven”
approach
can
contribute
to
producing
sufficiently
diverse
synthetic
training
data
for
Large
Language
Models
(LLMs)
are
currently
about
run
out
of
real
natural
language
texts.
In
our
paper,
we
explore
whether
personas
evoked
from
LLMs
through
HCI-style
descriptions
could
indeed
imitate
human-like
differences
in
authorship.
For
this
end,
ran
an
associative
experiment
with
50
human
participants
and
four
artificial
popular
LLM-based
services:
GPT-4(o)
YandexGPT
Pro.
each
five
stimuli
words
selected
university
websites’
homepages,
asked
both
groups
subjects
come
up
10
short
associations
(in
Russian).
We
then
used
cosine
similarity
Mahalanobis
distance
measure
between
association
lists
produced
by
different
humans
personas.
While
difference
significant
associators
gender
age
groups,
neither
case
ChatGPT
or
YandexGPT.
Our
findings
suggest
services
so
far
fall
at
imitating
thesauri
authors.
The
outcome
study
might
be
interest
computer
linguists,
as
well
AI/ML
scientists
prompt
engineers.
Language: Английский
Distinguishing Reality from AI: Approaches for Detecting Synthetic Content
Computers,
Journal Year:
2024,
Volume and Issue:
14(1), P. 1 - 1
Published: Dec. 24, 2024
The
advancement
of
artificial
intelligence
(AI)
technologies,
including
generative
pre-trained
transformers
(GPTs)
and
models
for
text,
image,
audio,
video
creation,
has
revolutionized
content
generation,
creating
unprecedented
opportunities
critical
challenges.
This
paper
systematically
examines
the
characteristics,
methodologies,
challenges
associated
with
detecting
synthetic
across
multiple
modalities,
to
safeguard
digital
authenticity
integrity.
Key
detection
approaches
reviewed
include
stylometric
analysis,
watermarking,
pixel
prediction
techniques,
dual-stream
networks,
machine
learning
models,
blockchain,
hybrid
approaches,
highlighting
their
strengths
limitations,
as
well
accuracy,
independent
accuracy
80%
analysis
up
92%
using
modalities
in
approaches.
effectiveness
these
techniques
is
explored
diverse
contexts,
from
identifying
deepfakes
media
AI-generated
scientific
texts.
Ethical
concerns,
such
privacy
violations,
algorithmic
bias,
false
positives,
overreliance
on
automated
systems,
are
also
critically
discussed.
Furthermore,
addresses
legal
regulatory
frameworks,
intellectual
property
emerging
legislation,
emphasizing
need
robust
governance
mitigate
misuse.
Real-world
examples
systems
analyzed
provide
practical
insights
into
implementation
Future
directions
developing
generalizable
adaptive
fostering
collaboration
between
stakeholders,
integrating
ethical
safeguards.
By
presenting
a
comprehensive
overview
AIGC
detection,
this
aims
inform
researchers,
policymakers,
practitioners
addressing
dual-edged
implications
AI-driven
creation.
Language: Английский
Exploring the Next Frontier in Wireless Communication: 5G and Beyond for Enhanced Reliability and Low Latency in IoT and Autonomous Technologies
Nanotechnology Perceptions,
Journal Year:
2024,
Volume and Issue:
unknown, P. 676 - 689
Published: Dec. 1, 2024
This
research
focuses
on
how
5G
and
beyond
technologies
might
be
the
game
changers
in
reliability,
low
latency,
efficiency,
improvement
of
IoT
autonomous
systems,
such
as
electric
vehicles.
It
addresses
advancements
6G-based
communication
networks
integrated
with
machine
learning
edge
computing
to
enhance
vehicle
performance,
energy
management,
vehicle-to-infrastructure
(V2I)
communication.
Extensive
experimentation
conducted
greatly
led
discovery
important
improvements
response
time.
Latency
was
reduced
by
much
45
per
cent
when
compared
4G
networks,
this
meant
that
6G
enabled
potential
increases
up
60
over
data
throughput
reliability
high-density
environments.
In
addition
that,
AI
application
towards
predictive
maintenance
battery
optimization
an
increase
30
for
applications
intelligence
a
more
sustainable
EV
system.
The
results
further
reveal
promise
AI-based
security
ML-based
25%
reduction
network
vulnerabilities
traditional
protocols.
inform
transformative
capability
next
generations
fulfil
their
scope
remodelling
future
vehicles
systems.
Future
will
focus
overcoming
present
infrastructure
deficiencies
improving
algorithms
behind
real-time
decision-making
processes
support
scalable,
energy-efficient,
secure
ecosystems.
Language: Английский
Multimodal Large Language Model-Based Fault Detection and Diagnosis in Context of Industry 4.0
Electronics,
Journal Year:
2024,
Volume and Issue:
13(24), P. 4912 - 4912
Published: Dec. 12, 2024
In
this
paper,
a
novel
multimodal
large
language
model-based
fault
detection
and
diagnosis
framework
that
addresses
the
limitations
of
traditional
approaches
is
proposed.
The
proposed
leverages
Generative
Pre-trained
Transformer-4-Preview
model
to
improve
its
scalability,
generalizability,
efficiency
in
handling
complex
systems
various
scenarios.
Moreover,
synthetic
datasets
generated
via
models
augment
knowledge
base
enhance
accuracy
imbalanced
framework,
hybrid
architecture
integrates
online
offline
processing,
combining
real-time
data
streams
with
fine-tuned
for
dynamic,
accurate,
context-aware
suited
industrial
settings,
particularly
focusing
on
security
concerns,
introduced.
This
comprehensive
approach
aims
address
challenges
advance
field
toward
more
adaptive
efficient
systems.
paper
presents
detailed
literature
review,
including
taxonomy
methods
their
applications
across
domains.
study
discusses
case
results
comparisons,
exploring
implications
future
developments
within
Industry
4.0
technologies.
Language: Английский
A Review of Large Language Models: Fundamental Architectures, Key Technological Evolutions, Interdisciplinary Technologies Integration, Optimization and Compression Techniques, Applications, and Challenges
Electronics,
Journal Year:
2024,
Volume and Issue:
13(24), P. 5040 - 5040
Published: Dec. 21, 2024
Large
language
model-related
technologies
have
shown
astonishing
potential
in
tasks
such
as
machine
translation,
text
generation,
logical
reasoning,
task
planning,
and
multimodal
alignment.
Consequently,
their
applications
continuously
expanded
from
natural
processing
to
computer
vision,
scientific
computing,
other
vertical
industry
fields.
This
rapid
surge
research
work
a
short
period
poses
significant
challenges
for
researchers
comprehensively
grasp
the
dynamics,
understand
key
technologies,
develop
field.
To
address
this,
this
paper
provides
comprehensive
review
of
on
large
models.
First,
it
organizes
reviews
background
current
status,
clarifying
definition
models
both
Chinese
English
communities.
Second,
analyzes
mainstream
infrastructure
briefly
introduces
optimization
methods
that
support
them.
Then,
conducts
detailed
intersections
between
interdisciplinary
contrastive
learning,
knowledge
enhancement,
retrieval
hallucination
dissolution,
recommendation
systems,
reinforcement
models,
agents,
pointing
out
valuable
ideas.
Finally,
deployment
identifies
limitations
they
face,
an
outlook
future
directions.
Our
aims
not
only
provide
systematic
but
also
focus
integration
with
hoping
ideas
inspiration
carry
secondary
development
Language: Английский