MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models

• URL: http://arxiv.org/abs/2409.19492v1
• Date: Sun, 29 Sep 2024 00:09:01 GMT
• Title: MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models
• Authors: Vibhor Agarwal, Yiqiao Jin, Mohit Chandra, Munmun De Choudhury, Srijan Kumar, Nishanth Sastry,
• Abstract summary: We conduct a pioneering study of hallucinations in LLM-generated responses to real-world healthcare queries from patients. We propose MedHalu, a carefully crafted first-of-its-kind medical hallucination dataset with a diverse range of health-related topics. We also introduce MedHaluDetect framework to evaluate capabilities of various LLMs in detecting hallucinations.
• Score: 26.464489158584463
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The remarkable capabilities of large language models (LLMs) in language understanding and generation have not rendered them immune to hallucinations. LLMs can still generate plausible-sounding but factually incorrect or fabricated information. As LLM-empowered chatbots become popular, laypeople may frequently ask health-related queries and risk falling victim to these LLM hallucinations, resulting in various societal and healthcare implications. In this work, we conduct a pioneering study of hallucinations in LLM-generated responses to real-world healthcare queries from patients. We propose MedHalu, a carefully crafted first-of-its-kind medical hallucination dataset with a diverse range of health-related topics and the corresponding hallucinated responses from LLMs with labeled hallucination types and hallucinated text spans. We also introduce MedHaluDetect framework to evaluate capabilities of various LLMs in detecting hallucinations. We also employ three groups of evaluators -- medical experts, LLMs, and laypeople -- to study who are more vulnerable to these medical hallucinations. We find that LLMs are much worse than the experts. They also perform no better than laypeople and even worse in few cases in detecting hallucinations. To fill this gap, we propose expert-in-the-loop approach to improve hallucination detection through LLMs by infusing expert reasoning. We observe significant performance gains for all the LLMs with an average macro-F1 improvement of 6.3 percentage points for GPT-4.

Related papers

• Look Within, Why LLMs Hallucinate: A Causal Perspective [16.874588396996764]
  Large language models (LLMs) are a milestone in generative artificial intelligence, achieving significant success in text comprehension and generation tasks. LLMs suffer from severe hallucination problems, posing significant challenges to the practical applications of LLMs. We propose a method to intervene in LLMs' self-attention layers and maintain their structures and sizes intact.
  arXiv  Detail & Related papers  (2024-07-14T10:47:44Z)
• 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)
• Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States [19.343629282494774]
  Large Language Models (LLMs) can make up answers that are not real, and this is known as hallucination. This research aims to see if, how, and to what extent LLMs are aware of hallucination.
  arXiv  Detail & Related papers  (2024-02-15T06:14:55Z)
• Hallucination Detection and Hallucination Mitigation: An Investigation [13.941799495842776]
  Large language models (LLMs) have achieved remarkable successes over the last two years in a range of different applications. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation.
  arXiv  Detail & Related papers  (2024-01-16T13:36:07Z)
• 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)
• Analyzing and Mitigating Object Hallucination in Large Vision-Language Models [110.12460299261531]
  Large vision-language models (LVLMs) have shown remarkable abilities in understanding visual information with human languages. LVLMs still suffer from object hallucination, which is the problem of generating descriptions that include objects that do not actually exist in the images. We propose a powerful algorithm, LVLM Hallucination Revisor (LURE), to rectify object hallucination in LVLMs by reconstructing less hallucinatory descriptions.
  arXiv  Detail & Related papers  (2023-10-01T18:10:53Z)
• Evaluation and Analysis of Hallucination in Large Vision-Language Models [49.19829480199372]
  Large Vision-Language Models (LVLMs) have recently achieved remarkable success. LVLMs are still plagued by the hallucination problem. Hallucination refers to the information of LVLMs' responses that does not exist in the visual input.
  arXiv  Detail & Related papers  (2023-08-29T08:51:24Z)
• HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models [146.87696738011712]
  Large language models (LLMs) are prone to generate hallucinations, i.e., content that conflicts with the source or cannot be verified by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation benchmark for Large Language Models (HaluEval)
  arXiv  Detail & Related papers  (2023-05-19T15:36:27Z)
• Evaluating Object Hallucination in Large Vision-Language Models [122.40337582958453]
  This work presents the first systematic study on object hallucination of large vision-language models (LVLMs) We find that LVLMs tend to generate objects that are inconsistent with the target images in the descriptions. We propose a polling-based query method called POPE to evaluate the object hallucination.
  arXiv  Detail & Related papers  (2023-05-17T16:34:01Z)

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.