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.

大语言模型（LLMs），尤其是适用于聊天的指导模型，已成为我们日常生活中的一部分，通过在一个地方提供简单的答案，使人们摆脱了从多个来源搜索、提取和整合信息的过程。然而，很多情况下，LLM 的回答是错误的，这限制了它们在实际场景中的适用性。因此，对评估和改进 LLM 的事实准确性的研究近年来引起了很多关注。在这项调查中，我们以批判性的角度分析现有的工作，旨在确定主要的挑战及其关联的原因，指出改进 LLM 的潜在解决方案，分析开放式文本生成的自动事实准确性评估的障碍，并展望未来研究的方向。