Resolving Internal Knowledge Conflicts in Pre-trained LLMs

Develop mechanisms to detect, manage, and resolve internal knowledge conflicts arising within large language models due to contradictory, time-varying, subjective, or uncertain information in web-scale pretraining corpora, enabling reliable selection and synthesis of answers.

Background

Web-scale corpora contain conflicting, outdated, subjective, and uncertain information, which leads to internal knowledge conflicts when models learn via probabilistic language modeling. The authors highlight the need for models to select, synthesize, and handle such conflicts—capabilities that are natural for humans but challenging for current LLMs without structured preprocessing or additional mechanisms.