Hallucination in Language Models

Follow this topic Ask about this topic

Momentum

13 papers in the last four weeks, up 160% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 187

All topics

Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor
Jul 21, 2025Siyuan Liu, Wenjing Liu, Zhiwei Xu +3LLM Hallucination Mitigation Hallucination in Language Models
College of Computer Science, Nankai University · College of intelligent Science and Technology, Inner Mongolia University of Technology · Institute of Computing Technology, Chinese Academy of Sciences +3
Popular but Wrong: Understanding and Mitigating LLM Overconfidence through Knowledge Popularity
May 23, 2025Shiyu Ni, Keping Bi, Jiafeng Guo +1LLM Hallucination Mitigation Confidence Estimation in Language Models
CAS Key Lab of Network Data Science and Technology, ICT, CAS · State Key Laboratory of AI Safety · University of Chinese Academy of Sciences
Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models
May 18, 2025Kai Tang, Jinhao You, Yichen Guo +8Vision-Language Models Hallucination in Language Models
Peking University · University of Pennsylvania · Nanyang Technological University +4
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs
Mar 3, 2025Tin Nguyen, Logan Bolton, Mohammad Reza Taesiri +2LLM Grounding LLM Answer Verification
Auburn University · University of Alberta
Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection
Feb 20, 2025Yihao Xue, Kristjan Greenewald, Youssef Mroueh +1Kernel Mean Embeddings Selective Prediction
Department of Computer Science University of California, Los Angeles · MIT-IBM Watson AI Lab · IBM Research
When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs
Date pendingShaowen Wang, Yiqi Dong, Ruinian Chang +4Hallucination in Language Models LLM Hallucination Detection
Institute for Interdisciplinary Information Sciences, Tsinghua University
MAD: Modality-Adaptive Decoding for Mitigating Cross-Modal Hallucinations in Multimodal Large Language Models
Date pendingSangyun Chung, Se Yeon Kim, Youngchae Chee +1Multimodal Robustness Hallucination in Language Models
Integrated Vision Language Lab, KAIST, South Korea

Jul 21, 2025, cs.LGJ/K move · Enter open · S save

Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor

Siyuan Liu, Wenjing Liu, Zhiwei Xu, Xin Wang+2

College of Computer Science, Nankai University · College of intelligent Science and Technology, Inner Mongolia University of Technology · Institute of Computing Technology, Chinese Academy of Sciences+3

LLM Hallucination Mitigation Hallucination in Language Models AI Agent Monitoring LLM Hallucination Detection LLM Agents LLM Agent Reliability

Open paper AskPDF arXiv

Empowered by large language models (LLMs), intelligent agents have become a popular paradigm for interacting with open environments to facilitate AI deployment. However, hallucinations generated by LLMs-where outputs are inconsistent with facts-pose a significant challenge, undermining the credibility of intelligent agents. Only if hallucinations can be mitigated, the intelligent agents can be used in real-world without any catastrophic risk. Therefore, effective detection and mitigation of hallucinations are crucial to ensure the dependability of agents. Unfortunately, the related approaches either depend on white-box access to LLMs or fail to accurately identify hallucinations. To address the challenge posed by hallucinations of intelligent agents, we present HalMit, a novel black-box watchdog framework that models the generalization bound of LLM-empowered agents and thus detect hallucinations without requiring internal knowledge of the LLM's architecture. Specifically, a probabilistic fractal sampling technique is proposed to generate a sufficient number of queries to trigger the incredible responses in parallel, efficiently identifying the generalization bound of the target agent. Experimental evaluations demonstrate that HalMit significantly outperforms existing approaches in hallucination monitoring. Its black-box nature and superior performance make HalMit a promising solution for enhancing the dependability of LLM-powered systems.

Momentum

Related topics

Latest papers 187