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 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.
@article{arxiv.2402.02420,
title = {Factuality of Large Language Models: A Survey},
author = {Yuxia Wang and Minghan Wang and Muhammad Arslan Manzoor and Fei Liu and Georgi Georgiev and Rocktim Jyoti Das and Preslav Nakov},
journal= {arXiv preprint arXiv:2402.02420},
year = {2024}
}