Factuality of Large Language Models: A Survey
It addresses the issue of unreliable information from LLMs for users and developers, but is incremental as it synthesizes existing work rather than introducing new methods.
This survey analyzes the problem of factual inaccuracies in large language models (LLMs), which limits their real-world use, by critically reviewing existing research to identify challenges, causes, and potential solutions.
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.