Knowledge Conflicts for LLMs: A Survey
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.
Paper Type: Long
Research Area: Interpretability and Analysis of Models for NLP
Research Area Keywords: calibration/uncertainty, knowledge tracing/discovering/inducing, robustness
Contribution Types: Surveys
Languages Studied: English
Submission Number: 4061
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