id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.11312 | AI Generations: From AI 1.0 to AI 4.0 | cs.AI | This paper proposes that Artificial Intelligence (AI) progresses through
several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI),
AI 3.0 (Physical AI), and now a speculative AI 4.0 (Conscious AI). Each of
these AI generations is driven by shifting priorities among algorithms,
computing power, an... |
2502.11323 | A statistical theory of overfitting for imbalanced classification | math.ST cs.LG stat.ML stat.TH | Classification with imbalanced data is a common challenge in data analysis,
where certain classes (minority classes) account for a small fraction of the
training data compared with other classes (majority classes). Classical
statistical theory based on large-sample asymptotics and finite-sample
corrections is often i... |
2502.11324 | Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical
Study | stat.ML cs.LG | Robust statistics aims to compute quantities to represent data where a
fraction of it may be arbitrarily corrupted. The most essential statistic is
the mean, and in recent years, there has been a flurry of theoretical
advancement for efficiently estimating the mean in high dimensions on corrupted
data. While several ... |
2502.11329 | Differentially private fine-tuned NF-Net to predict GI cancer type | cs.CV | Based on global genomic status, the cancer tumor is classified as
Microsatellite Instable (MSI) and Microsatellite Stable (MSS). Immunotherapy is
used to diagnose MSI, whereas radiation and chemotherapy are used for MSS.
Therefore, it is significant to classify a gastro-intestinal (GI) cancer tumor
into MSI vs. MSS t... |
2502.11330 | System Message Generation for User Preferences using Open-Source Models | cs.CL cs.AI | System messages play a crucial role in interactions with large language
models (LLMs), often serving as prompts to initiate conversations. Through
system messages, users can assign specific roles, perform intended tasks,
incorporate background information, specify various output formats and
communication styles. Desp... |
2502.11331 | Transfer Learning of CATE with Kernel Ridge Regression | stat.ME cs.LG stat.ML | The proliferation of data has sparked significant interest in leveraging
findings from one study to estimate treatment effects in a different target
population without direct outcome observations. However, the transfer learning
process is frequently hindered by substantial covariate shift and limited
overlap between ... |
2502.11333 | Inverse Flow and Consistency Models | cs.LG cs.AI | Inverse generation problems, such as denoising without ground truth
observations, is a critical challenge in many scientific inquiries and
real-world applications. While recent advances in generative models like
diffusion models, conditional flow matching, and consistency models achieved
impressive results by casting... |
2502.11335 | Personalized Ranking on Cascading Behavior Graphs for Accurate
Multi-Behavior Recommendation | cs.IR | Multi-behavior recommendation predicts items a user may purchase by analyzing
diverse behaviors like viewing, adding to a cart, and purchasing. Existing
methods fall into two categories: representation learning and graph ranking.
Representation learning generates user and item embeddings to capture latent
interaction... |
2502.11336 | ExaGPT: Example-Based Machine-Generated Text Detection for Human
Interpretability | cs.CL | Detecting texts generated by Large Language Models (LLMs) could cause grave
mistakes due to incorrect decisions, such as undermining student's academic
dignity. LLM text detection thus needs to ensure the interpretability of the
decision, which can help users judge how reliably correct its prediction is.
When humans ... |
2502.11337 | A Comparison of Human and Machine Learning Errors in Face Recognition | cs.HC cs.CV cs.CY | Machine learning applications in high-stakes scenarios should always operate
under human oversight. Developing an optimal combination of human and machine
intelligence requires an understanding of their complementarities, particularly
regarding the similarities and differences in the way they make mistakes. We
perfor... |
2502.11338 | WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld
Radiographic Testing | cs.CV | Radiographic testing is a fundamental non-destructive evaluation technique
for identifying weld defects and assessing quality in industrial applications
due to its high-resolution imaging capabilities. Over the past decade, deep
learning techniques have significantly advanced weld defect identification in
radiographi... |
2502.11340 | S2TX: Cross-Attention Multi-Scale State-Space Transformer for Time
Series Forecasting | cs.LG | Time series forecasting has recently achieved significant progress with
multi-scale models to address the heterogeneity between long and short range
patterns. Despite their state-of-the-art performance, we identify two potential
areas for improvement. First, the variates of the multivariate time series are
processed ... |
2502.11345 | Hierarchical Graph Topic Modeling with Topic Tree-based Transformer | cs.CL | Textual documents are commonly connected in a hierarchical graph structure
where a central document links to others with an exponentially growing
connectivity. Though Hyperbolic Graph Neural Networks (HGNNs) excel at
capturing such graph hierarchy, they cannot model the rich textual semantics
within documents. Moreov... |
2502.11346 | Power-Measurement-Based Channel Autocorrelation Estimation for
IRS-Assisted Wideband Communications | cs.IT math.IT | Channel state information (CSI) is essential to the performance optimization
of intelligent reflecting surface (IRS)-aided wireless communication systems.
However, the passive and frequency-flat reflection of IRS, as well as the
high-dimensional IRS-reflected channels, have posed practical challenges for
efficient IR... |
2502.11349 | Biases in Edge Language Models: Detection, Analysis, and Mitigation | cs.LG cs.PF stat.ML | The integration of large language models (LLMs) on low-power edge devices
such as Raspberry Pi, known as edge language models (ELMs), has introduced
opportunities for more personalized, secure, and low-latency language
intelligence that is accessible to all. However, the resource constraints
inherent in edge devices ... |
2502.11352 | A Framework for Learning Scoring Rules in Autonomous Driving Planning
Systems | cs.RO cs.LG | In autonomous driving systems, motion planning is commonly implemented as a
two-stage process: first, a trajectory proposer generates multiple candidate
trajectories, then a scoring mechanism selects the most suitable trajectory for
execution. For this critical selection stage, rule-based scoring mechanisms are
parti... |
2502.11355 | "Nuclear Deployed!": Analyzing Catastrophic Risks in Decision-making of
Autonomous LLM Agents | cs.CL cs.AI cs.CR cs.CY | Large language models (LLMs) are evolving into autonomous decision-makers,
raising concerns about catastrophic risks in high-stakes scenarios,
particularly in Chemical, Biological, Radiological and Nuclear (CBRN) domains.
Based on the insight that such risks can originate from trade-offs between the
agent's Helpful, ... |
2502.11356 | SAIF: A Sparse Autoencoder Framework for Interpreting and Steering
Instruction Following of Language Models | cs.LG cs.AI cs.CL | The ability of large language models (LLMs) to follow instructions is crucial
for their practical applications, yet the underlying mechanisms remain poorly
understood. This paper presents a novel framework that leverages sparse
autoencoders (SAE) to interpret how instruction following works in these
models. We demons... |
2502.11357 | Explorer: Scaling Exploration-driven Web Trajectory Synthesis for
Multimodal Web Agents | cs.AI cs.HC | Recent success in large multimodal models (LMMs) has sparked promising
applications of agents capable of autonomously completing complex web tasks.
While open-source LMM agents have made significant advances in offline
evaluation benchmarks, their performance still falls substantially short of
human-level capabilitie... |
2502.11358 | Mimicking the Familiar: Dynamic Command Generation for Information Theft
Attacks in LLM Tool-Learning System | cs.AI cs.CR | Information theft attacks pose a significant risk to Large Language Model
(LLM) tool-learning systems. Adversaries can inject malicious commands through
compromised tools, manipulating LLMs to send sensitive information to these
tools, which leads to potential privacy breaches. However, existing attack
approaches are... |
2502.11360 | GeoDANO: Geometric VLM with Domain Agnostic Vision Encoder | cs.CV cs.CL | We introduce GeoDANO, a geometric vision-language model (VLM) with a
domain-agnostic vision encoder, for solving plane geometry problems. Although
VLMs have been employed for solving geometry problems, their ability to
recognize geometric features remains insufficiently analyzed. To address this
gap, we propose a ben... |
2502.11361 | VLDBench: Vision Language Models Disinformation Detection Benchmark | cs.CL | The rapid rise of AI-generated content has made detecting disinformation
increasingly challenging. In particular, multimodal disinformation, i.e.,
online posts-articles that contain images and texts with fabricated information
are specially designed to deceive. While existing AI safety benchmarks
primarily address bi... |
2502.11362 | Teleportation With Null Space Gradient Projection for Optimization
Acceleration | cs.LG | Optimization techniques have become increasingly critical due to the
ever-growing model complexity and data scale. In particular, teleportation has
emerged as a promising approach, which accelerates convergence of gradient
descent-based methods by navigating within the loss invariant level set to
identify parameters ... |
2502.11364 | Blessing of Multilinguality: A Systematic Analysis of Multilingual
In-Context Learning | cs.CL | While multilingual large language models generally perform adequately, and
sometimes even rival English performance on high-resource languages (HRLs),
they often significantly underperform on low-resource languages (LRLs). Among
several prompting strategies aiming at bridging the gap, multilingual
in-context learning... |
2502.11367 | Sparse Autoencoder Features for Classifications and Transferability | cs.LG cs.AI cs.CL | Sparse Autoencoders (SAEs) provide potentials for uncovering structured,
human-interpretable representations in Large Language Models (LLMs), making
them a crucial tool for transparent and controllable AI systems. We
systematically analyze SAE for interpretable feature extraction from LLMs in
safety-critical classifi... |
2502.11368 | LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case
Study of L2 Graduate-Level Academic English Writing | cs.CL cs.AI | The paper explores the performance of LLMs in the context of
multi-dimensional analytic writing assessments, i.e. their ability to provide
both scores and comments based on multiple assessment criteria. Using a corpus
of literature reviews written by L2 graduate students and assessed by human
experts against 9 analyt... |
2502.11369 | Physics-Informed Gaussian Process Classification for Constraint-Aware
Alloy Design | cond-mat.mtrl-sci cs.LG | Alloy design can be framed as a constraint-satisfaction problem. Building on
previous methodologies, we propose equipping Gaussian Process Classifiers
(GPCs) with physics-informed prior mean functions to model the boundaries of
feasible design spaces. Through three case studies, we highlight the utility of
informativ... |
2502.11370 | HI-GVF: Shared Control based on Human-Influenced Guiding Vector Fields
for Human-multi-robot Cooperation | cs.RO | Human-multi-robot shared control leverages human decision-making and robotic
autonomy to enhance human-robot collaboration. While widely studied, existing
systems often adopt a leader-follower model, limiting robot autonomy to some
extent. Besides, a human is required to directly participate in the motion
control of ... |
2502.11371 | RAG vs. GraphRAG: A Systematic Evaluation and Key Insights | cs.IR | Retrieval-Augmented Generation (RAG) enhances the performance of LLMs across
various tasks by retrieving relevant information from external sources,
particularly on text-based data. For structured data, such as knowledge graphs,
GraphRAG has been widely used to retrieve relevant information. However, recent
studies h... |
2502.11372 | Weibull Processes in Network Degree Distributions | cs.SI physics.soc-ph | This study examines degree distributions in two large collaboration networks:
the Microsoft Academic Graph (1800-2020) and Internet Movie Database
(1900-2020), comprising $2.72 \times 10^8$ and $1.88 \times 10^6$ nodes
respectively. Statistical comparison using $\chi^2$ measures showed that
Weibull distributions fit ... |
2502.11374 | Leave No One Behind: Enhancing Diversity While Maintaining Accuracy in
Social Recommendation | cs.IR | Social recommendation, a branch of algorithms that utilizes social connection
information to construct recommender systems, has demonstrated its
effectiveness in enhancing recommendation accuracy. However, apart from
accuracy, the diversity of recommendations also plays a critical role in user
engagement. Unfortunate... |
2502.11375 | Robot Deformable Object Manipulation via NMPC-generated Demonstrations
in Deep Reinforcement Learning | cs.RO cs.LG | In this work, we conducted research on deformable object manipulation by
robots based on demonstration-enhanced reinforcement learning (RL). To improve
the learning efficiency of RL, we enhanced the utilization of demonstration
data from multiple aspects and proposed the HGCR-DDPG algorithm. It uses a
novel high-dime... |
2502.11377 | PrivilegedDreamer: Explicit Imagination of Privileged Information for
Rapid Adaptation of Learned Policies | cs.RO cs.LG | Numerous real-world control problems involve dynamics and objectives affected
by unobservable hidden parameters, ranging from autonomous driving to robotic
manipulation, which cause performance degradation during sim-to-real transfer.
To represent these kinds of domains, we adopt hidden-parameter Markov decision
proc... |
2502.11379 | CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language
Models | cs.CR cs.AI cs.CL | Despite explicit alignment efforts for large language models (LLMs), they can
still be exploited to trigger unintended behaviors, a phenomenon known as
"jailbreaking." Current jailbreak attack methods mainly focus on discrete
prompt manipulations targeting closed-source LLMs, relying on manually crafted
prompt templa... |
2502.11380 | Exploring the Small World of Word Embeddings: A Comparative Study on
Conceptual Spaces from LLMs of Different Scales | cs.CL | A conceptual space represents concepts as nodes and semantic relatedness as
edges. Word embeddings, combined with a similarity metric, provide an effective
approach to constructing such a space. Typically, embeddings are derived from
traditional distributed models or encoder-only pretrained models, whose
objectives d... |
2502.11381 | Without Paired Labeled Data: An End-to-End Self-Supervised Paradigm for
UAV-View Geo-Localization | cs.CV cs.AI | UAV-View Geo-Localization (UVGL) aims to ascertain the precise location of a
UAV by retrieving the most similar GPS-tagged satellite image. However,
existing methods predominantly rely on supervised learning paradigms that
necessitate annotated paired data for training, which incurs substantial
annotation costs and i... |
2502.11382 | A Physics-Informed Blur Learning Framework for Imaging Systems | cs.CV | Accurate blur estimation is essential for high-performance imaging across
various applications. Blur is typically represented by the point spread
function (PSF). In this paper, we propose a physics-informed PSF learning
framework for imaging systems, consisting of a simple calibration followed by a
learning process. ... |
2502.11386 | Intelligent Mobile AI-Generated Content Services via Interactive Prompt
Engineering and Dynamic Service Provisioning | cs.NI cs.LG | Due to massive computational demands of large generative models, AI-Generated
Content (AIGC) can organize collaborative Mobile AIGC Service Providers (MASPs)
at network edges to provide ubiquitous and customized content generation for
resource-constrained users. However, such a paradigm faces two significant
challeng... |
2502.11387 | RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and
Instruction-Following | cs.CL | Role-playing is important for Large Language Models (LLMs) to follow diverse
instructions while maintaining role identity and the role's pre-defined ability
limits. Existing role-playing datasets mostly contribute to controlling role
style and knowledge boundaries, but overlook role-playing in
instruction-following s... |
2502.11390 | MARS: Mesh AutoRegressive Model for 3D Shape Detailization | cs.CV | State-of-the-art methods for mesh detailization predominantly utilize
Generative Adversarial Networks (GANs) to generate detailed meshes from coarse
ones. These methods typically learn a specific style code for each category or
similar categories without enforcing geometry supervision across different
Levels of Detai... |
2502.11393 | HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the
Robustness of LLMs in Commonsense Reasoning | cs.CL | Large language models (LLMs) have shown remarkable capabilities in
commonsense reasoning; however, some variations in questions can trigger
incorrect responses. Do these models truly understand commonsense knowledge, or
just memorize expression patterns? To investigate this question, we present the
first extensive ro... |
2502.11394 | Oversmoothing as Loss of Sign: Towards Structural Balance in Graph
Neural Networks | cs.LG | Oversmoothing is a common issue in graph neural networks (GNNs), where node
representations become excessively homogeneous as the number of layers
increases, resulting in degraded performance. Various strategies have been
proposed to combat oversmoothing in practice, yet they are based on different
heuristics and lac... |
2502.11396 | Maintenance of Structural Hole Spanners in Dynamic Networks | cs.SI | Structural Hole (SH) spanners are the set of users who bridge different
groups of users and are vital in numerous applications. Despite their
importance, existing work for identifying SH spanners focuses only on static
networks. However, real-world networks are highly dynamic where the underlying
structure of the net... |
2502.11400 | Revisiting Robust RAG: Do We Still Need Complex Robust Training in the
Era of Powerful LLMs? | cs.CL | Retrieval-augmented generation (RAG) systems often suffer from performance
degradation when encountering noisy or irrelevant documents, driving
researchers to develop sophisticated training strategies to enhance their
robustness against such retrieval noise. However, as large language models
(LLMs) continue to advanc... |
2502.11401 | Following the Autoregressive Nature of LLM Embeddings via Compression
and Alignment | cs.CL | A new trend uses LLMs as dense text encoders via contrastive learning.
However, since LLM embeddings predict the probability distribution of the next
token, they are inherently generative and distributive, conflicting with
contrastive learning, which requires embeddings to capture full-text semantics
and align via co... |
2502.11404 | ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large
Language Models | cs.CL | Tool learning has emerged as a crucial capability for large language models
(LLMs) to solve complex real-world tasks through interaction with external
tools. Existing approaches face significant challenges, including reliance on
hand-crafted prompts, difficulty in multi-step planning, and lack of precise
error diagno... |
2502.11405 | LayAlign: Enhancing Multilingual Reasoning in Large Language Models via
Layer-Wise Adaptive Fusion and Alignment Strategy | cs.CL | Despite being pretrained on multilingual corpora, large language models
(LLMs) exhibit suboptimal performance on low-resource languages. Recent
approaches have leveraged multilingual encoders alongside LLMs by introducing
trainable parameters connecting the two models. However, these methods
typically focus on the en... |
2502.11408 | Precise GPS-Denied UAV Self-Positioning via Context-Enhanced Cross-View
Geo-Localization | cs.CV | Image retrieval has been employed as a robust complementary technique to
address the challenge of Unmanned Aerial Vehicles (UAVs) self-positioning.
However, most existing methods primarily focus on localizing objects captured
by UAVs through complex part-based representations, often overlooking the
unique challenges ... |
2502.11410 | Structure based SAT dataset for analysing GNN generalisation | cs.LG | Satisfiability (SAT) solvers based on techniques such as conflict driven
clause learning (CDCL) have produced excellent performance on both synthetic
and real world industrial problems. While these CDCL solvers only operate on a
per-problem basis, graph neural network (GNN) based solvers bring new benefits
to the fie... |
2502.11411 | Detecting and Filtering Unsafe Training Data via Data Attribution | cs.LG | Large language models (LLMs) are vulnerable to unsafe training data that even
small amounts of unsafe data can lead to harmful model behaviors. Detecting and
filtering such unsafe training data is essential for trustworthy model
development. Current state-of-the-art (SOTA) approaches typically rely on
training modera... |
2502.11413 | Statistical Query Hardness of Multiclass Linear Classification with
Random Classification Noise | cs.LG stat.ML | We study the task of Multiclass Linear Classification (MLC) in the
distribution-free PAC model with Random Classification Noise (RCN).
Specifically, the learner is given a set of labeled examples $(x, y)$, where
$x$ is drawn from an unknown distribution on $R^d$ and the labels are generated
by a multiclass linear cla... |
2502.11414 | Unbiased Learning to Rank with Query-Level Click Propensity Estimation:
Beyond Pointwise Observation and Relevance | cs.IR | Most existing unbiased learning-to-rank (ULTR) approaches are based on the
user examination hypothesis, which assumes that users will click a result only
if it is both relevant and observed (typically modeled by position). However,
in real-world scenarios, users often click only one or two results after
examining mul... |
2502.11417 | DiSCo: Device-Server Collaborative LLM-Based Text Streaming Services | cs.LG cs.DC | The rapid rise of large language models (LLMs) in text streaming services has
introduced significant cost and Quality of Experience (QoE) challenges in
serving millions of daily requests, especially in meeting Time-To-First-Token
(TTFT) and Time-Between-Token (TBT) requirements for real-time interactions.
Our real-wo... |
2502.11418 | TimeCAP: Learning to Contextualize, Augment, and Predict Time Series
Events with Large Language Model Agents | cs.AI cs.LG | Time series data is essential in various applications, including climate
modeling, healthcare monitoring, and financial analytics. Understanding the
contextual information associated with real-world time series data is often
essential for accurate and reliable event predictions. In this paper, we
introduce TimeCAP, a... |
2502.11419 | InsBank: Evolving Instruction Subset for Ongoing Alignment | cs.CL | Large language models (LLMs) typically undergo instruction tuning to enhance
alignment. Recent studies emphasize that quality and diversity of instruction
data are more crucial than quantity, highlighting the need to select diverse,
high-quality subsets to reduce training costs. However, how to evolve these
selected ... |
2502.11420 | Training-Free Guidance Beyond Differentiability: Scalable Path Steering
with Tree Search in Diffusion and Flow Models | cs.LG | Training-free guidance enables controlled generation in diffusion and flow
models, but most existing methods assume differentiable objectives and rely on
gradients. This work focuses on training-free guidance addressing challenges
from non-differentiable objectives and discrete data distributions. We propose
an algor... |
2502.11422 | Planning of Heuristics: Strategic Planning on Large Language Models with
Monte Carlo Tree Search for Automating Heuristic Optimization | cs.AI | Heuristics have achieved great success in solving combinatorial optimization
problems (COPs). However, heuristics designed by humans require too much domain
knowledge and testing time. Given the fact that Large Language Models (LLMs)
possess strong capabilities to understand and generate content, and a knowledge
base... |
2502.11423 | Exploring Persona Sentiment Sensitivity in Personalized Dialogue
Generation | cs.CL | Personalized dialogue systems have advanced considerably with the integration
of user-specific personas into large language models (LLMs). However, while
LLMs can effectively generate personalized responses, the influence of persona
sentiment on dialogue quality remains underexplored. In this work, we conduct a
large... |
2502.11425 | Counterfactual-Consistency Prompting for Relative Temporal Understanding
in Large Language Models | cs.CL cs.AI | Despite the advanced capabilities of large language models (LLMs), their
temporal reasoning ability remains underdeveloped. Prior works have highlighted
this limitation, particularly in maintaining temporal consistency when
understanding events. For example, models often confuse mutually exclusive
temporal relations ... |
2502.11426 | Verti-Bench: A General and Scalable Off-Road Mobility Benchmark for
Vertically Challenging Terrain | cs.RO | Recent advancement in off-road autonomy has shown promises in deploying
autonomous mobile robots in outdoor off-road environments. Encouraging results
have been reported from both simulated and real-world experiments. However,
unlike evaluating off-road perception tasks on static datasets, benchmarking
off-road mobil... |
2502.11427 | Do we Really Need Visual Instructions? Towards Visual Instruction-Free
Fine-tuning for Large Vision-Language Models | cs.CL cs.CV | Visual instruction tuning has become the predominant technology in eliciting
the multimodal task-solving capabilities of large vision-language models
(LVLMs). Despite the success, as visual instructions require images as the
input, it would leave the gap in inheriting the task-solving capabilities from
the backbone L... |
2502.11429 | What's in a Query: Polarity-Aware Distribution-Based Fair Ranking | cs.LG cs.CY | Machine learning-driven rankings, where individuals (or items) are ranked in
response to a query, mediate search exposure or attention in a variety of
safety-critical settings. Thus, it is important to ensure that such rankings
are fair. Under the goal of equal opportunity, attention allocated to an
individual on a r... |
2502.11431 | Any Information Is Just Worth One Single Screenshot: Unifying Search
With Visualized Information Retrieval | cs.CL | With the popularity of multimodal techniques, it receives growing interests
to acquire useful information in visual forms. In this work, we formally define
an emerging IR paradigm called \textit{Visualized Information Retrieval}, or
\textbf{Vis-IR}, where multimodal information, such as texts, images, tables
and char... |
2502.11433 | FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning
for Financial Trading | cs.AI cs.CE q-fin.TR | Large language models (LLMs) fine-tuned on multimodal financial data have
demonstrated impressive reasoning capabilities in various financial tasks.
However, they often struggle with multi-step, goal-oriented scenarios in
interactive financial markets, such as trading, where complex agentic
approaches are required to... |
2502.11435 | SMART: Self-Aware Agent for Tool Overuse Mitigation | cs.AI cs.CL cs.LG | Current Large Language Model (LLM) agents demonstrate strong reasoning and
tool use capabilities, but often lack self-awareness, failing to balance these
approaches effectively. This imbalance leads to Tool Overuse, where models
unnecessarily rely on external tools for tasks solvable with parametric
knowledge, increa... |
2502.11436 | ADO: Automatic Data Optimization for Inputs in LLM Prompts | cs.LG | This study explores a novel approach to enhance the performance of Large
Language Models (LLMs) through the optimization of input data within prompts.
While previous research has primarily focused on refining instruction
components and augmenting input data with in-context examples, our work
investigates the potentia... |
2502.11437 | Learning Dexterous Bimanual Catch Skills through Adversarial-Cooperative
Heterogeneous-Agent Reinforcement Learning | cs.RO cs.AI | Robotic catching has traditionally focused on single-handed systems, which
are limited in their ability to handle larger or more complex objects. In
contrast, bimanual catching offers significant potential for improved dexterity
and object handling but introduces new challenges in coordination and control.
In this pa... |
2502.11438 | SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example
Selection for Text-to-SQL | cs.CL | Text-to-SQL aims to convert natural language questions into executable SQL
queries. While previous approaches, such as skeleton-masked selection, have
demonstrated strong performance by retrieving similar training examples to
guide large language models (LLMs), they struggle in real-world scenarios where
such example... |
2502.11439 | An Efficient Row-Based Sparse Fine-Tuning | cs.CL cs.AI cs.LG | Fine-tuning is an important step in adapting foundation models such as large
language models to downstream tasks. To make this step more accessible to users
with limited computational budgets, it is crucial to develop fine-tuning
methods that are memory and computationally efficient. Sparse Fine-tuning (SFT)
and Low-... |
2502.11440 | Medical Image Registration Meets Vision Foundation Model: Prototype
Learning and Contour Awareness | cs.CV | Medical image registration is a fundamental task in medical image analysis,
aiming to establish spatial correspondences between paired images. However,
existing unsupervised deformable registration methods rely solely on
intensity-based similarity metrics, lacking explicit anatomical knowledge,
which limits their acc... |
2502.11441 | Which Retain Set Matters for LLM Unlearning? A Case Study on Entity
Unlearning | cs.CL | Large language models (LLMs) risk retaining unauthorized or sensitive
information from their training data, which raises privacy concerns. LLM
unlearning seeks to mitigate these risks by selectively removing specified data
while maintaining overall model performance. However, most existing work focus
on methods to ac... |
2502.11442 | Multi-Turn Multi-Modal Question Clarification for Enhanced
Conversational Understanding | cs.IR cs.AI cs.CL cs.LG | Conversational query clarification enables users to refine their search
queries through interactive dialogue, improving search effectiveness.
Traditional approaches rely on text-based clarifying questions, which often
fail to capture complex user preferences, particularly those involving visual
attributes. While rece... |
2502.11444 | Does RAG Really Perform Bad For Long-Context Processing? | cs.CL | The efficient processing of long context poses a serious challenge for large
language models (LLMs). Recently, retrieval-augmented generation (RAG) has
emerged as a promising strategy for this problem, as it enables LLMs to make
selective use of the long context for efficient computation. However, existing
RAG approa... |
2502.11447 | Does Editing Provide Evidence for Localization? | cs.LG cs.AI | A basic aspiration for interpretability research in large language models is
to "localize" semantically meaningful behaviors to particular components within
the LLM. There are various heuristics for finding candidate locations within
the LLM. Once a candidate localization is found, it can be assessed by editing
the i... |
2502.11448 | AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety
Detection | cs.AI | The rapid advancements in Large Language Models (LLMs) have enabled their
deployment as autonomous agents for handling complex tasks in dynamic
environments. These LLMs demonstrate strong problem-solving capabilities and
adaptability to multifaceted scenarios. However, their use as agents also
introduces significant ... |
2502.11449 | Tractable General Equilibrium | cs.GT cs.CE econ.TH | We study Walrasian economies (or general equilibrium models) and their
solution concept, the Walrasian equilibrium. A key challenge in this domain is
identifying price-adjustment processes that converge to equilibrium. One such
process, t\^atonnement, is an auction-like algorithm first proposed in 1874 by
L\'eon Walr... |
2502.11450 | Fishing For Cheap And Efficient Pruners At Initialization | cs.LG cs.AI | Pruning offers a promising solution to mitigate the associated costs and
environmental impact of deploying large deep neural networks (DNNs).
Traditional approaches rely on computationally expensive trained models or
time-consuming iterative prune-retrain cycles, undermining their utility in
resource-constrained sett... |
2502.11451 | From Personas to Talks: Revisiting the Impact of Personas on
LLM-Synthesized Emotional Support Conversations | cs.CL | The rapid advancement of Large Language Models (LLMs) has revolutionized the
generation of emotional support conversations (ESC), offering scalable
solutions with reduced costs and enhanced data privacy. This paper explores the
role of personas in the creation of ESC by LLMs. Our research utilizes
established psychol... |
2502.11453 | Connector-S: A Survey of Connectors in Multi-modal Large Language Models | cs.LG cs.AI | With the rapid advancements in multi-modal large language models (MLLMs),
connectors play a pivotal role in bridging diverse modalities and enhancing
model performance. However, the design and evolution of connectors have not
been comprehensively analyzed, leaving gaps in understanding how these
components function a... |
2502.11454 | UniCBE: An Uniformity-driven Comparing Based Evaluation Framework with
Unified Multi-Objective Optimization | cs.CL | Human preference plays a significant role in measuring large language models
and guiding them to align with human values. Unfortunately, current
comparing-based evaluation (CBE) methods typically focus on a single
optimization objective, failing to effectively utilize scarce yet valuable
preference signals. To addres... |
2502.11456 | Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning
Network for Semi-supervised 3D Medical Image Segmentation | cs.CV cs.AI | Semi-supervised 3D medical image segmentation aims to achieve accurate
segmentation using few labelled data and numerous unlabelled data. The main
challenge in the design of semi-supervised learning methods consists in the
effective use of the unlabelled data for training. A promising solution
consists of ensuring co... |
2502.11457 | Aligning Sentence Simplification with ESL Learner's Proficiency for
Language Acquisition | cs.CL cs.AI | Text simplification is crucial for improving accessibility and comprehension
for English as a Second Language (ESL) learners. This study goes a step further
and aims to facilitate ESL learners' language acquisition by simplification.
Specifically, we propose simplifying complex sentences to appropriate levels
for lea... |
2502.11458 | Towards Efficient Pre-training: Exploring FP4 Precision in Large
Language Models | cs.LG cs.AI | The burgeoning computational demands for training large language models
(LLMs) necessitate efficient methods, including quantized training, which
leverages low-bit arithmetic operations to reduce costs. While FP8 precision
has shown potential, leveraging FP4 remains challenging due to inherent
quantization errors and... |
2502.11459 | Towards Responsible and Fair Data Science: Resource Allocation for
Inclusive and Sustainable Analytics | cs.DB | This project addresses the challenges of responsible and fair resource
allocation in data science (DS), focusing on DS queries evaluation. Current DS
practices often overlook the broader socio-economic, environmental, and ethical
implications, including data sovereignty, fairness, and inclusivity. By
integrating a de... |
2502.11460 | UnitCoder: Scalable Iterative Code Synthesis with Unit Test Guidance | cs.CL cs.SE | Large Language Models (LLMs) have demonstrated remarkable capabilities in
various tasks, yet code generation remains a major challenge. Current
approaches for obtaining high-quality code data primarily focus on (i)
collecting large-scale pre-training data and (ii) synthesizing instruction data
through prompt engineer... |
2502.11461 | Doppler Correspondence: Non-Iterative Scan Matching With Doppler
Velocity-Based Correspondence | cs.RO | Achieving successful scan matching is essential for LiDAR odometry. However,
in challenging environments with adverse weather conditions or repetitive
geometric patterns, LiDAR odometry performance is degraded due to incorrect
scan matching. Recently, the emergence of frequency-modulated continuous wave
4D LiDAR and ... |
2502.11462 | LMFCA-Net: A Lightweight Model for Multi-Channel Speech Enhancement with
Efficient Narrow-Band and Cross-Band Attention | eess.AS cs.LG cs.SD | Deep learning based end-to-end multi-channel speech enhancement methods have
achieved impressive performance by leveraging sub-band, cross-band, and spatial
information. However, these methods often demand substantial computational
resources, limiting their practicality on terminal devices. This paper presents
a ligh... |
2502.11465 | All Models Are Miscalibrated, But Some Less So: Comparing Calibration
with Conditional Mean Operators | stat.ML cs.LG | When working in a high-risk setting, having well calibrated probabilistic
predictive models is a crucial requirement. However, estimators for calibration
error are not always able to correctly distinguish which model is better
calibrated. We propose the \emph{conditional kernel calibration error} (CKCE)
which is base... |
2502.11466 | GiFT: Gibbs Fine-Tuning for Code Generation | cs.LG cs.CL cs.SE | Training Large Language Models (LLMs) with synthetic data is a prevalent
practice in code generation. A key approach is self-training, where LLMs are
iteratively trained on self-generated correct code snippets. In this case, the
self-generated codes are drawn from a conditional distribution, conditioned on
a specific... |
2502.11467 | Approximation of Permutation Invariant Polynomials by Transformers:
Efficient Construction in Column-Size | cs.LG math.FA | Transformers are a type of neural network that have demonstrated remarkable
performance across various domains, particularly in natural language processing
tasks. Motivated by this success, research on the theoretical understanding of
transformers has garnered significant attention. A notable example is the
mathemati... |
2502.11468 | Semantically Robust Unsupervised Image Translation for Paired Remote
Sensing Images | cs.CV | Image translation for change detection or classification in bi-temporal
remote sensing images is unique. Although it can acquire paired images, it is
still unsupervised. Moreover, strict semantic preservation in translation is
always needed instead of multimodal outputs. In response to these problems,
this paper prop... |
2502.11469 | If Attention Serves as a Cognitive Model of Human Memory Retrieval, What
is the Plausible Memory Representation? | cs.CL | Recent work in computational psycholinguistics has revealed intriguing
parallels between attention mechanisms and human memory retrieval, focusing
primarily on Transformer architectures that operate on token-level
representations. However, computational psycholinguistic research has also
established that syntactic st... |
2502.11470 | Optimized detection of cyber-attacks on IoT networks via hybrid deep
learning models | cs.CR cs.AI | The rapid expansion of Internet of Things (IoT) devices has increased the
risk of cyber-attacks, making effective detection essential for securing IoT
networks. This work introduces a novel approach combining Self-Organizing Maps
(SOMs), Deep Belief Networks (DBNs), and Autoencoders to detect known and
previously uns... |
2502.11471 | GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language
for Knowledge Graph Completion | cs.CL cs.IR | Knowledge Graph Completion (KGC), which aims to infer missing or incomplete
facts, is a crucial task for KGs. However, integrating the vital structural
information of KGs into Large Language Models (LLMs) and outputting predictions
deterministically remains challenging. To address this, we propose a new method
called... |
2502.11476 | FastMCTS: A Simple Sampling Strategy for Data Synthesis | cs.CL | Synthetic high-quality multi-step reasoning data can significantly enhance
the performance of large language models on various tasks. However, most
existing methods rely on rejection sampling, which generates trajectories
independently and suffers from inefficiency and imbalanced sampling across
problems of varying d... |
2502.11477 | Learning to Sample Effective and Diverse Prompts for Text-to-Image
Generation | cs.CV | Recent advances in text-to-image diffusion models have achieved impressive
image generation capabilities. However, it remains challenging to control the
generation process with desired properties (e.g., aesthetic quality, user
intention), which can be expressed as black-box reward functions. In this
paper, we focus o... |
2502.11478 | TAPS: Throat and Acoustic Paired Speech Dataset for Deep Learning-Based
Speech Enhancement | cs.SD cs.LG eess.AS | In high-noise environments such as factories, subways, and busy streets,
capturing clear speech is challenging due to background noise. Throat
microphones provide a solution with their noise-suppressing properties,
reducing the noise while recording speech. However, a significant limitation
remains: high-frequency in... |
2502.11480 | Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian
Optimization Perspective | cs.LG stat.ML | Offline model-based reinforcement learning (MBRL) serves as a competitive
framework that can learn well-performing policies solely from pre-collected
data with the help of learned dynamics models. To fully unleash the power of
offline MBRL, model selection plays a pivotal role in determining the dynamics
model utiliz... |
2502.11481 | Variable-frame CNNLSTM for Breast Nodule Classification using Ultrasound
Videos | cs.CV cs.AI | The intersection of medical imaging and artificial intelligence has become an
important research direction in intelligent medical treatment, particularly in
the analysis of medical images using deep learning for clinical diagnosis.
Despite the advances, existing keyframe classification methods lack extraction
of time... |
2502.11482 | DATA: Decomposed Attention-based Task Adaptation for Rehearsal-Free
Continual Learning | cs.LG cs.AI cs.CL | Continual learning (CL) is essential for Large Language Models (LLMs) to
adapt to evolving real-world demands, yet they are susceptible to catastrophic
forgetting (CF). While traditional CF solutions rely on expensive data
rehearsal, recent rehearsal-free methods employ model-based and
regularization-based strategies... |
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