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This survey addresses the crucial issue of factuality in Large LanguageModels (LLMs). As LLMs find applications across diverse domains, thereliability and accuracy of their outputs become vital. We define theFactuality Issue as the probability of LLMs to produce content inconsistentwith established facts. We first delve into the implications of theseinaccuracies, highlighting the potential consequences and challenges posed byfactual errors in LLM outputs. Subsequently, we analyze the mechanisms throughwhich LLMs store and process facts, seeking the primary causes of factualerrors. Our discussion then transitions to methodologies for evaluating LLMfactuality, emphasizing key metrics, benchmarks, and studies. We furtherexplore strategies for enhancing LLM factuality, including approaches tailoredfor specific domains. We focus two primary LLM configurations standalone LLMsand Retrieval-Augmented LLMs that utilizes external data, we detail theirunique challenges and potential enhancements. Our survey offers a structuredguide for researchers aiming to fortify the factual reliability of LLMs.

Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity