An Audit on the Perspectives and Challenges of Hallucinations in NLP

Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta, Heidi Biggs, Mukund Srinath, Koustava Goswami, Sarah Rajtmajer, Shomir Wilson


Abstract
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.
Anthology ID:
2024.emnlp-main.375
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6528–6548
Language:
URL:
https://aclanthology.org/2024.emnlp-main.375
DOI:
Bibkey:
Cite (ACL):
Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta, Heidi Biggs, Mukund Srinath, Koustava Goswami, Sarah Rajtmajer, and Shomir Wilson. 2024. An Audit on the Perspectives and Challenges of Hallucinations in NLP. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6528–6548, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
An Audit on the Perspectives and Challenges of Hallucinations in NLP (Narayanan Venkit et al., EMNLP 2024)
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https://aclanthology.org/2024.emnlp-main.375.pdf
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