Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models

• URL: http://arxiv.org/abs/2403.06448v2
• Date: Mon, 10 Jun 2024 05:48:30 GMT
• Title: Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
• Authors: Weihang Su, Changyue Wang, Qingyao Ai, Yiran HU, Zhijing Wu, Yujia Zhou, Yiqun Liu,
• Abstract summary: Hallucinations in large language models (LLMs) produce responses that are coherent but factually inaccurate. We present MIND, an unsupervised training framework that leverages the internal states of LLMs for real-time hallucination detection. We also present HELM, a new benchmark for evaluating hallucination detection across multiple LLMs.
• Score: 12.27217471495276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hallucinations in large language models (LLMs) refer to the phenomenon of LLMs producing responses that are coherent yet factually inaccurate. This issue undermines the effectiveness of LLMs in practical applications, necessitating research into detecting and mitigating hallucinations of LLMs. Previous studies have mainly concentrated on post-processing techniques for hallucination detection, which tend to be computationally intensive and limited in effectiveness due to their separation from the LLM's inference process. To overcome these limitations, we introduce MIND, an unsupervised training framework that leverages the internal states of LLMs for real-time hallucination detection without requiring manual annotations. Additionally, we present HELM, a new benchmark for evaluating hallucination detection across multiple LLMs, featuring diverse LLM outputs and the internal states of LLMs during their inference process. Our experiments demonstrate that MIND outperforms existing state-of-the-art methods in hallucination detection.

Related papers

• LLM Hallucination Reasoning with Zero-shot Knowledge Test [10.306443936136425]
  We introduce a new task, Hallucination Reasoning, which classifies LLM-generated text into one of three categories: aligned, misaligned, and fabricated. Our experiments conducted on new datasets demonstrate the effectiveness of our method in hallucination reasoning.
  arXiv  Detail & Related papers  (2024-11-14T18:55:26Z)
• SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection [10.54378596443678]
  Large language models (LLMs) are highly capable but face latency challenges in real-time applications. This study optimize the real-time interpretable hallucination detection by introducing effective prompting techniques.
  arXiv  Detail & Related papers  (2024-08-22T22:13:13Z)
• Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models [70.19081534515371]
  Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks. They generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. We propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers.
  arXiv  Detail & Related papers  (2024-07-04T18:47:42Z)
• LLM Internal States Reveal Hallucination Risk Faced With a Query [62.29558761326031]
  Humans have a self-awareness process that allows us to recognize what we don't know when faced with queries. This paper investigates whether Large Language Models can estimate their own hallucination risk before response generation. By a probing estimator, we leverage LLM self-assessment, achieving an average hallucination estimation accuracy of 84.32% at run time.
  arXiv  Detail & Related papers  (2024-07-03T17:08:52Z)
• PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics [51.17512229589]
  PoLLMgraph is a model-based white-box detection and forecasting approach for large language models. We show that hallucination can be effectively detected by analyzing the LLM's internal state transition dynamics. Our work paves a new way for model-based white-box analysis of LLMs, motivating the research community to further explore, understand, and refine the intricate dynamics of LLM behaviors.
  arXiv  Detail & Related papers  (2024-04-06T20:02:20Z)
• Retrieve Only When It Needs: Adaptive Retrieval Augmentation for Hallucination Mitigation in Large Language Models [68.91592125175787]
  Hallucinations pose a significant challenge for the practical implementation of large language models (LLMs) We present Rowen, a novel approach that enhances LLMs with a selective retrieval augmentation process tailored to address hallucinations.
  arXiv  Detail & Related papers  (2024-02-16T11:55:40Z)
• The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models [134.6697160940223]
  hallucination poses great challenge to trustworthy and reliable deployment of large language models. Three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them. This work presents a systematic empirical study on LLM hallucination, focused on the the three aspects of hallucination detection, source and mitigation.
  arXiv  Detail & Related papers  (2024-01-06T12:40:45Z)
• A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions [40.79317187623401]
  The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP) LLMs are prone to hallucination, generating plausible yet nonfactual content. This phenomenon raises significant concerns over the reliability of LLMs in real-world information retrieval systems.
  arXiv  Detail & Related papers  (2023-11-09T09:25:37Z)
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models [116.01843550398183]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks. LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
  arXiv  Detail & Related papers  (2023-09-03T16:56:48Z)
• Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models [11.497989461290793]
  Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) Open-source LLMs with fewer parameters often suffer from severe hallucinations compared to their larger counterparts. This paper focuses on measuring and reducing hallucinations in BLOOM 7B, a representative of such weaker open-source LLMs.
  arXiv  Detail & Related papers  (2023-08-22T20:12:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.