Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models

• URL: http://arxiv.org/abs/2407.04121v1
• Date: Thu, 4 Jul 2024 18:47:42 GMT
• Title: Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models
• Authors: Yuyan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao,
• Abstract summary: 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.
• Score: 70.19081534515371
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks, including question answering and dialogue systems. However, a major drawback of LLMs is the issue of hallucination, where they generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. In this paper, we propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers. RelD is trained on the constructed RelQA, a bilingual question-answering dialogue dataset along with answers generated by LLMs and a comprehensive set of metrics. Our experimental results demonstrate that the proposed RelD successfully detects hallucination in the answers generated by diverse LLMs. Moreover, it performs well in distinguishing hallucination in LLMs' generated answers from both in-distribution and out-of-distribution datasets. Additionally, we also conduct a thorough analysis of the types of hallucinations that occur and present valuable insights. This research significantly contributes to the detection of reliable answers generated by LLMs and holds noteworthy implications for mitigating hallucination in the future work.

Related papers

• A Survey of Hallucination in Large Visual Language Models [48.794850395309076]
  The existence of hallucinations has limited the potential and practical effectiveness of LVLM in various fields. The structure of LVLMs and main causes of hallucination generation are introduced. The available hallucination evaluation benchmarks for LVLMs are presented.
  arXiv  Detail & Related papers  (2024-10-20T10:58:58Z)
• LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models [96.64960606650115]
  LongHalQA is an LLM-free hallucination benchmark that comprises 6K long and complex hallucination text. LongHalQA is featured by GPT4V-generated hallucinatory data that are well aligned with real-world scenarios.
  arXiv  Detail & Related papers  (2024-10-13T18:59:58Z)
• Mitigating Entity-Level Hallucination in Large Language Models [11.872916697604278]
  This paper proposes Dynamic Retrieval Augmentation based on hallucination Detection (DRAD) as a novel method to detect and mitigate hallucinations in Large Language Models (LLMs) Experiment results show that DRAD demonstrates superior performance in both detecting and mitigating hallucinations in LLMs.
  arXiv  Detail & Related papers  (2024-07-12T16:47:34Z)
• Exploring and Evaluating Hallucinations in LLM-Powered Code Generation [14.438161741833687]
  Large Language Models (LLMs) produce outputs that deviate from users' intent, exhibit internal inconsistencies, or misalign with factual knowledge. Existing work mainly focuses on investing the hallucination in the domain of natural language generation. We conduct a thematic analysis of the LLM-generated code to summarize and categorize the hallucinations present in it. We propose HalluCode, a benchmark for evaluating the performance of code LLMs in recognizing hallucinations.
  arXiv  Detail & Related papers  (2024-04-01T07:31:45Z)
• 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)
• DelucionQA: Detecting Hallucinations in Domain-specific Question Answering [22.23664008053246]
  Hallucination is a well-known phenomenon in text generated by large language models (LLMs) We introduce a dataset, DelucionQA, that captures hallucinations made by retrieval-augmented LLMs for a domain-specific QA task. We propose a set of hallucination detection methods to serve as baselines for future works from the research community.
  arXiv  Detail & Related papers  (2023-12-08T17:41:06Z)
• 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)
• AutoHall: Automated Hallucination Dataset Generation for Large Language Models [56.92068213969036]
  This paper introduces a method for automatically constructing model-specific hallucination datasets based on existing fact-checking datasets called AutoHall. We also propose a zero-resource and black-box hallucination detection method based on self-contradiction.
  arXiv  Detail & Related papers  (2023-09-30T05:20:02Z)
• 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)

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.