Factuality of Large Language Models: A Survey

Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu, Georgi Nenkov Georgiev, Rocktim Jyoti Das, Preslav Nakov


Abstract
Large language models (LLMs), especially when instruction-tuned for chat, have become part of our daily lives, freeing people from the process of searching, extracting, and integrating information from multiple sources by offering a straightforward answer to a variety of questions in a single place. Unfortunately, in many cases, LLM responses are factually incorrect, which limits their applicability in real-world scenarios. As a result, research on evaluating and improving the factuality of LLMs has attracted a lot of research attention recently. In this survey, we critically analyze existing work with the aim to identify the major challenges and their associated causes, pointing out to potential solutions for improving the factuality of LLMs, and analyzing the obstacles to automated factuality evaluation for open-ended text generation. We further offer an outlook on where future research should go.
Anthology ID:
2024.emnlp-main.1088
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:
19519–19529
Language:
URL:
https://aclanthology.org/2024.emnlp-main.1088
DOI:
10.18653/v1/2024.emnlp-main.1088
Bibkey:
Cite (ACL):
Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu, Georgi Nenkov Georgiev, Rocktim Jyoti Das, and Preslav Nakov. 2024. Factuality of Large Language Models: A Survey. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 19519–19529, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Factuality of Large Language Models: A Survey (Wang et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-main.1088.pdf