Daily Arxiv

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Interleaving Reasoning for Better Text-to-Image Generation

Barycentric Neural Networks and Length-Weighted Persistent Entropy Loss: A Green Geometric and Topological Framework for Function Approximation

Signal-Based Malware Classification Using 1D CNNs

Toward a Metrology for Artificial Intelligence: Hidden-Rule Environments and Reinforcement Learning

BranchGRPO: Stable and Efficient GRPO with Structured Branching in Diffusion Models

LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding

No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomy

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning?

HodgeFormer: Transformers for Learnable Operators on Triangular Meshes through Data-Driven Hodge Matrices

CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models

Pilot Study on Generative AI and Critical Thinking in Higher Education Classrooms

ZkLoRA: Fine-Tuning Large Language Models with Verifiable Security via Zero-Knowledge Proofs

EmbodiedOneVision: Interleaved Vision-Text-Action Pretraining for General Robot Control

Ultra-Low-Latency Spiking Neural Networks with Temporal-Dependent Integrate-and-Fire Neuron Model for Objects Detection

Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models

A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems

Trust but Verify! A Survey on Verification Design for Test-time Scaling

Research on Conversational Recommender System Considering Consumer Types

A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges

Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control

Enhancing Dialogue Annotation with Speaker Characteristics Leveraging a Frozen LLM

A Mixed User-Centered Approach to Enable Augmented Intelligence in Intelligent Tutoring Systems: The Case of MathAIde app

Meaning-infused grammar: Gradient Acceptability Shapes the Geometric Representations of Constructions in LLMs

MoRPI-PINN: A Physics-Informed Framework for Mobile Robot Pure Inertial Navigation

Conditional Video Generation for High-Efficiency Video Compression

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges

Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models

Language Models Might Not Understand You: Evaluating Theory of Mind via Story Prompting

From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat Explanations

HueManity: Probing Fine-Grained Visual Perception in MLLMs

Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments

Localizing Persona Representations in LLMs

Multi-output Classification using a Cross-talk Architecture for Compound Fault Diagnosis of Motors in Partially Labeled Condition

SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning

Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives

Towards Visuospatial Cognition via Hierarchical Fusion of Visual Experts

Visuospatial Cognitive Assistant

Overflow Prevention Enhances Long-Context Recurrent LLMs

GRADA: Graph-based Reranking against Adversarial Documents Attack

OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models

Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices

Unlearning vs. Obfuscation: Are We Truly Removing Knowledge?

Llama-Nemotron: Efficient Reasoning Models

Tripartite-GraphRAG via Plugin Ontologies

DMS-Net:Dual-Modal Multi-Scale Siamese Network for Binocular Fundus Image Classification

Enhancing Traffic Incident Response through Sub-Second Temporal Localization with HybridMamba

Audio-centric Video Understanding Benchmark without Text Shortcut

The Model Hears You: Audio Language Model Deployments Should Consider the Principle of Least Privilege

Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution

DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation

MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention

Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection

VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR Identification

Cardiverse: Harnessing LLMs for Novel Card Game Prototyping

TrojanRobot: Physical-world Backdoor Attacks Against VLM-based Robotic Manipulation

Automatically Detecting Online Deceptive Patterns

TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection

Solving Truly Massive Budgeted Monotonic POMDPs with Oracle-Guided Meta-Reinforcement Learning

CTourLLM: Enhancing LLMs with Chinese Tourism Knowledge

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference

SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents

MSRFormer: Road Network Representation Learning using Multi-scale Feature Fusion of Heterogeneous Spatial Interactions

Attention of a Kiss: Exploring Attention Maps in Video Diffusion for XAIxArts

EvoEmo: Towards Evolved Emotional Policies for LLM Agents in Multi-Turn Negotiation

AI-SearchPlanner: Modular Agentic Search via Pareto-Optimal Multi-Objective Reinforcement Learning

MaRVL-QA: A Benchmark for Mathematical Reasoning over Visual Landscapes

Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond

CountQA: How Well Do MLLMs Count in the Wild?

ASP-FZN: A Translation-based Constraint Answer Set Solver

MedGellan: LLM-Generated Medical Guidance to Support Physicians

Modeling the Diachronic Evolution of Legal Norms: An LRMoo-Based, Component-Level, Event-Centric Approach to Legal Knowledge Graphs

Addition in Four Movements: Mapping Layer-wise Information Trajectories in LLMs

GeoChain: Multimodal Chain-of-Thoughts for Geographic Reasoning

Automatic Reward Shaping from Confounded Offline Data

Visualizing Thought: Conceptual Diagrams Enable Robust Combinatorial Planning in LMMs

COMMA: A Communicative Multimodal Multi-Agent Benchmark

PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents

Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism

Self-Emotion-Mediated Exploration in Artificial Intelligence Mirrors: Findings from Cognitive Psychology

Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Search

ACE and Diverse Generalization via Selective Disagreement

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges

ImportSnare: Directed "Code Manual" Hijacking in Retrieval-Augmented Code Generation

Breaking Android with AI: A Deep Dive into LLM-Powered Exploitation

Accelerating Local AI on Consumer GPUs: A Hardware-Aware Dynamic Strategy for YOLOv10s

GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models

Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation

Uncovering Scaling Laws for Large Language Models via Inverse Problems

Active Membership Inference Test (aMINT): Enhancing Model Auditability with Multi-Task Learning

Deep Learning-Based Burned Area Mapping Using Bi-Temporal Siamese Networks and AlphaEarth Foundation Datasets

Small Open Models Achieve Near Parity with Large Models in Low Resource Literary Translation at a Fraction of the Cost

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models

Enhanced SegNet with Integrated Grad-CAM for Interpretable Retinal Layer Segmentation in OCT Images

Individual utilities of life satisfaction reveal inequality aversion unrelated to political alignment

XSRD-Net: EXplainable Stroke Relapse Detection

Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-tuning

What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects

Spectral and Rhythm Feature Performance Evaluation for Category and Class Level Audio Classification with Deep Convolutional Neural Networks

Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review

Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems

GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models

Created by

Haebom

Author

Tuo Wang, Adithya Kulkarni, Tyler Cody, Peter A. Beling, Yujun Yan, Dawei Zhou

Outline

This paper emphasizes the importance of uncertainty estimation for improving the reliability of large-scale language models (LLMs) and introduces the GENUINE (Graph-ENhanced mUlti-level uncertainty Estimation) framework to address the problem of overlooking semantic dependencies due to token-level probability measurements, which are Limitations in existing methods. GENUINE performs structure-aware uncertainty quantification by utilizing dependency parse trees and hierarchical graph pooling, and effectively models semantic and structural relationships through supervised learning to improve reliability assessment. Experimental results on various NLP tasks demonstrate the effectiveness of graph-based uncertainty modeling, achieving up to 29% higher AUROC and reducing calibration errors by more than 15% compared to existing semantic entropy-based approaches. The source code is available at https://github.com/ODYSSEYWT/GUQ .

GitHub - ODYSSEYWT/GUQ

Contribute to ODYSSEYWT/GUQ development by creating an account on GitHub.

github.com

Takeaways, Limitations

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Takeaways:

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We demonstrate that graph-based uncertainty modeling can improve the reliability of LLM.

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We experimentally demonstrate that this approach provides more accurate and compensated uncertainty estimation than the existing token-by-token approach.

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More sophisticated uncertainty measures are possible by taking into account semantic and structural relationships.

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Provides a general framework applicable to a variety of NLP tasks.

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Limitations:

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The performance improvements of the proposed method may be limited to specific datasets or tasks.

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Computational costs may be higher than conventional methods.

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The results may be affected by the correctness of the dependency parse tree.

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Further research is needed on the generalization performance for different types of LLMs.

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