Where Knowledge Collides: A Mechanistic Study of Intra-Memory Knowledge Conflict in Language Models

• URL: http://arxiv.org/abs/2601.09445v1
• Date: Wed, 14 Jan 2026 12:45:52 GMT
• Title: Where Knowledge Collides: A Mechanistic Study of Intra-Memory Knowledge Conflict in Language Models
• Authors: Minh Vu Pham, Hsuvas Borkakoty, Yufang Hou,
• Abstract summary: In language models (LMs) intra-memory knowledge conflict largely arises when inconsistent information about the same event is encoded within the model's parametric knowledge.<n>We use mechanistic interpretability methods to identify where and how conflicting knowledge from pre-training data is encoded within LMs.<n>Our findings contribute to a growing body of evidence that specific internal components of a language model are responsible for encoding conflicting knowledge from pre-training.
• Score: 8.965740058804197
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In language models (LMs), intra-memory knowledge conflict largely arises when inconsistent information about the same event is encoded within the model's parametric knowledge. While prior work has primarily focused on resolving conflicts between a model's internal knowledge and external resources through approaches such as fine-tuning or knowledge editing, the problem of localizing conflicts that originate during pre-training within the model's internal representations remain unexplored. In this work, we design a framework based on mechanistic interpretability methods to identify where and how conflicting knowledge from the pre-training data is encoded within LMs. Our findings contribute to a growing body of evidence that specific internal components of a language model are responsible for encoding conflicting knowledge from pre-training, and we demonstrate how mechanistic interpretability methods can be leveraged to causally intervene in and control conflicting knowledge at inference time.

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