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.
BibTeX
@inproceedings{narayanan-venkit-etal-2024-audit,
title = "An Audit on the Perspectives and Challenges of Hallucinations in {NLP}",
author = "Narayanan Venkit, Pranav and
Chakravorti, Tatiana and
Gupta, Vipul and
Biggs, Heidi and
Srinath, Mukund and
Goswami, Koustava and
Rajtmajer, Sarah and
Wilson, Shomir",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.375/",
doi = "10.18653/v1/2024.emnlp-main.375",
pages = "6528--6548"
}