Knowledge Conflicts for LLM s: A Survey (2024)

Abstract: This survey provides an in-depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge. Our focus is on three categories of knowledge conflicts: context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of LLMs, especially in real-world applications where noise and misinformation are common. By categorizing these conflicts, exploring the causes, examining the behaviors of LLMs under such conflicts, and reviewing available solutions, this survey aims to shed light on strategies for improving the robustness of LLMs, thereby serving as a valuable resource for advancing research in this evolving area.

## introduction large language models (llms; @xcite ; openai 2024) are renowned for encapsulating a vast repository of world knowledge @xcite , referred to as parametric knowledge. these models excel in various knowledge-intensive tasks. meanwhile, llms continue to engage with external contextual knowledge after deployed @xcite , including user prompts @xcite , documents from the web @xcite , or tools @xcite . integrating contextual knowledge into llms enables them to keep abreast of current events @xcite and generate more accurate responses @xcite , yet it risks conflicting due to the rich knowledge sources. the discrepancies among the contexts and the model's parametric knowledge are referred to as knowledge brazil holds the record for the most fifa world cup wins… …i think argentina has won the most championships. italy is the most successful national team in the history of the world cup, having won four titles @xcite @xcite . as of my last update in april 2023, the national team with the most fifa world cup championships is brazil. they have won the tournament a total of five times. ## context-memory conflict llms are characterized by fixed parametric knowledge, a result of the substantial pertaining process @xcite @xcite . this static parametric knowledge stands in stark contrast to the dynamic nature of external information, which evolves at a rapid pace @xcite . ## inter-context conflict inter-context conflicts manifest in llms when incorporating conflicting segments among external information sources, a challenge accentuated by the advent of rag techniques. ## intra-memory conflict consistent llm outputs for identical inputs are essential. however, intra-memory conflicts, where llms generate differing responses to similar inputs, undermine their reliability and utility by introducing undesirable uncertainty. ## conclusion we extensively investigate knowledge conflicts for llms, shedding light on the categorization, causes, behavior analyses, and mitigations. we demonstrate that the type of conflict significantly influences a model's behavior and that these conflicts exhibit complex interplays. existing solutions, often focused on artificial scenarios and relying on priors, lack the granularity and breadth needed to address the increasing complexity of knowledge conflicts in real-world applications. given the growing use of retrieval-augmented llms, we anticipate that knowledge conflicts will keep increasing in complexity, underscoring the need for more comprehensive research.