Studying Large Language Model Behaviors Under Context-Memory Conflicts With Real Documents
Retrieval-augmented generation (RAG) mitigates many problems of fully parametric language models, such as temporal degradation, hallucinations, and lack of grounding. In RAG, the model's knowledge can be updated from documents provided in context. This leads to cases of conflict between the mod...
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Zusammenfassung: | Retrieval-augmented generation (RAG) mitigates many problems of fully
parametric language models, such as temporal degradation, hallucinations, and
lack of grounding. In RAG, the model's knowledge can be updated from documents
provided in context. This leads to cases of conflict between the model's
parametric knowledge and the contextual information, where the model may not
always update its knowledge. Previous work studied context-memory knowledge
conflicts by creating synthetic documents that contradict the model's correct
parametric answers. We present a framework for studying such knowledge
conflicts in a realistic setup. We update incorrect parametric knowledge using
real conflicting documents. This reflects how knowledge conflicts arise in
practice. In this realistic scenario, we find that knowledge updates fail less
often than previously reported. In cases where the models still fail to update
their answers, we find a parametric bias: the incorrect parametric answer
appearing in context makes the knowledge update likelier to fail. These results
suggest that the factual parametric knowledge of LLMs can negatively influence
their reading abilities and behaviors. Our code is available at
https://github.com/kortukov/realistic_knowledge_conflicts/ . |
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DOI: | 10.48550/arxiv.2404.16032 |