arXiv AI

INTRYGUE: Induction-Aware Entropy Gating for Reliable RAG Uncertainty Estimation

arXiv:2603. 21607v2 Announce Type: replace Abstract: While retrieval-augmented generation (RAG) significantly improves the factual reliability of LLMs, it does not eliminate hallucinations, so robust uncertainty quantification (UQ) remains essential.

arXiv AI

Jun 2

TriLens: Per-Layer Logit-Lens Entropy for White-Box Hallucination Detection

arXiv:2606. 01033v1 Announce Type: new Abstract: When a language model hallucinates, the final answer is wrong, but the mistake is not necessarily invisible inside the model.

By Bohan Yang, Yijun Gong, Zhi Zhang, Ge Zhang, Wenpeng Xing, Meng Han

llms benchmarks safety

arXiv AI

Jul 21

Diversity-Oriented Fine-Tuning for Uncertainty-Based Hallucination Detection

arXiv:2607. 16643v1 Announce Type: new Abstract: Existing hallucination detection methods are typically conducted at the inference stage, without making any modifications to the model itself.

By Qiuyuan Li, Hongliang Dai, Piji Li

reinforcement-learning fine-tuning safety

arXiv AI

Aug 12

Actionable Hallucination Detection: Translating Latent Uncertainty into Agentic Critique

arXiv:2608. 10430v1 Announce Type: cross Abstract: Large Language Models (LLMs) deployed as AI agents frequently exhibit user specification-grounding failures, executing hallucinated, undesired actions to force a resolution rather than expressing uncertainty.

By Sanidhya Vijayvargiya, Rahul Lokesh

llms agents diffusion robotics fine-tuning efficiency safety

arXiv AI

Aug 5

LogitScope: A Framework for Analyzing LLM Uncertainty Through Information Metrics

arXiv:2603. 24929v2 Announce Type: replace Abstract: Understanding and quantifying uncertainty in large language model (LLM) outputs is critical for reliable deployment.

By Farhan Ahmed, Yuya Jeremy Ong, Chad DeLuca

llms safety

arXiv AI

Aug 5

UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space

arXiv:2608. 03817v1 Announce Type: cross Abstract: Large vision--language models (LVLMs) demonstrate strong multimodal reasoning capabilities but remain prone to hallucination, where model predictions are not grounded in visual evidence.

By Amir Mohammad Ezzati, Kiyan Rezaee, Bardiya Kariminia, Mohamad Amin Yousefi, Asal Mohammadjafari Mamaqani, Behrad Samimi, Mohammad Hossein Rohban

llms multimodal benchmarks safety

arXiv AI

Jun 9

BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models

arXiv:2606. 07528v1 Announce Type: cross Abstract: Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to reliable deployment.

By Naveen Bera, Pulijala Sai Nikhila, Kondaguduru Abhiram, Shaik Gayaz Ali, Shoaib Sadiq Salehmohamed, Shaik Mohammed Omar, Jinal Prashant Thakkar, Hansika Aredla, Shalmali Ayachit

llms rag benchmarks safety

arXiv AI

Jul 21

Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain

arXiv:2603. 21693v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have shown strong potential for medical Visual Question Answering (VQA), yet they remain prone to hallucinations, defined as generating responses that contradict the input image, posing serious risks in clinical settings.

By Mohammad Asadi, Tahoura Nedaee, Jack W. O'Sullivan, Euan Ashley, Ehsan Adeli

llms nlp multimodal benchmarks safety

Hugging Face Trending Papers

Jun 23

Grad Detect: Gradient-Based Hallucination Detection in LLMs

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet they remain prone to generating hallucinations. Detecting these hallucinations is critical for deploying LLMs reliably in high-stakes applications.

llms benchmarks safety

arXiv AI

Jul 28

TRE: Training-Free Hallucination Detection for Diffusion Language Models

arXiv:2607. 22661v1 Announce Type: new Abstract: Diffusion large language models (D-LLMs) have recently gained increasing attention, yet their reliability is significantly hindered by the hallucination problem.

By Pengcheng Weng, Yanyu Qian, Yue Tan, Yixin Liu

llms diffusion safety

arXiv AI

Jun 24

Grad Detect: Gradient-Based Hallucination Detection in LLMs

arXiv:2606. 24790v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet they remain prone to generating hallucinations.

By Anand Kamat, Daniel Blake, Brent M. Werness

llms benchmarks safety

arXiv Machine Learning

Jun 19

Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence

arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.

By Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee

llms benchmarks safety

arXiv AI

Jun 10

Integrating Local and Global Entropy for Uncertainty Quantification in LLMs

arXiv:2606. 09875v1 Announce Type: cross Abstract: Large language models hallucinate confidently, making uncertainty quantification (UQ) essential for reliable deployment.

By Johanne Medina, Tianyi Zhou, Keivin Isufaj, Aristides Gionis, Sanjay Chawla

llms benchmarks

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