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An Audit on the Perspectives and Challenges of Hallucinations in NLP

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arxiv 2404.07461 v2 pith:YGB73RAW submitted 2024-04-11 cs.CL cs.AI

Pranav Narayanan Venkit , Tatiana Chakravorti , Vipul Gupta , Heidi Biggs , Mukund Srinath , Koustava Goswami , Sarah Rajtmajer , Shomir Wilson This is my paper · ORCID
classification cs.CLcs.AI
keywords hallucinationauditchallengesexaminationfieldliteratureperspectivessurvey
verification ladder T0 review T1 audit T2 compute T3 formal
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We audit how hallucination in large language models (LLMs) is characterized in peer-reviewed literature, using a critical examination of 103 publications across NLP research. Through the examination of the literature, we identify a lack of agreement with the term `hallucination' in the field of NLP. Additionally, to compliment our audit, we conduct a survey with 171 practitioners from the field of NLP and AI to capture varying perspectives on hallucination. Our analysis calls for the necessity of explicit definitions and frameworks outlining hallucination within NLP, highlighting potential challenges, and our survey inputs provide a thematic understanding of the influence and ramifications of hallucination in society.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence

    cs.CL 2025-09 conditional novelty 6.0 of 10

    An audit framework and empirical study showing that generative search engines and deep research agents frequently produce one-sided answers and weakly supported citations, with citation accuracy between 40 and 80%.

  2. Social Scientists on the Role of AI in Research

    cs.CY 2025-06 conditional novelty 6.0 of 10

    Randomized survey wording makes social scientists report more familiarity but less trust in "AI" than in "machine learning", with ethical concerns concentrated on generative AI.

  3. Hallucination Detection with Small Language Models

    cs.CL 2025-06 reject novelty 5.0 of 10

    A multi-small-model ensemble with sentence splitting, z-score normalization, and harmonic mean detects hallucinations in RAG answers with a reported 10% F1 gain over single-model baselines.

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