Knowledge Boundary of Large Language Models: A Survey
This work addresses the need for a clearer framework to study LLM limitations, which is crucial for researchers and practitioners in AI to improve model reliability, but it is incremental as it synthesizes existing research rather than introducing new methods.
This survey tackles the problem of defining and understanding the knowledge boundaries of large language models (LLMs), which cause issues like generating untruthful responses, by proposing a comprehensive definition and taxonomy to categorize knowledge into four types and systematically reviewing the field.
Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, leading to undesired behaviors such as generating untruthful and inaccurate responses. This highlights the critical need to understand the knowledge boundary of LLMs, a concept that remains inadequately defined in existing research. In this survey, we propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types. Using this foundation, we systematically review the field through three key lenses: the motivation for studying LLM knowledge boundaries, methods for identifying these boundaries, and strategies for mitigating the challenges they present. Finally, we discuss open challenges and potential research directions in this area. We aim for this survey to offer the community a comprehensive overview, facilitate access to key issues, and inspire further advancements in LLM knowledge research.