Knowledge Conflicts for LLMs
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A survey that maps the landscape of knowledge conflicts in LLMs, covering how they arise, how models behave under them, and how to mitigate them.
Three conflict types: Context-memory (retrieved context disagrees with parametric knowledge), inter-context (multiple retrieved documents contradict each other), and intra-memory (the model's own parameters encode contradictions).
Causes: The survey traces conflicts to data quality, retrieval noise, outdated parametric knowledge, and pretraining contradictions - giving a shared vocabulary for prior work.
Model behavior: Catalogs how LLMs choose between conflicting sources and how that behavior shifts with model size, training, and prompting.
Mitigation directions: Reviews calibration-based, training-based, and prompting-based interventions for reducing conflict-induced errors, pointing to open evaluation gaps.
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