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.
BibTeX
@inproceedings{wang-etal-2024-factuality,
title = "Factuality of Large Language Models: A Survey",
author = "Wang, Yuxia and
Wang, Minghan and
Manzoor, Muhammad Arslan and
Liu, Fei and
Georgiev, Georgi Nenkov and
Das, Rocktim Jyoti and
Nakov, Preslav",
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.1088/",
doi = "10.18653/v1/2024.emnlp-main.1088",
pages = "19519--19529"
}