knowledge-conflicts

[NeurIPS 2025] ParamMute (a.k.a. PIP-KAG): suppressing knowledge-critical FFNs for faithful retrieval-augmented generation (RAG knowledge conflicts)

The Knowledge Lifecycle of Large Language Models: a unified five-stage framework for how LLMs acquire, store, retrieve, update, and forget knowledge, with the Provenance Vector proposal and the reproducible Lifecycle Desynchronization metric.

Official implementation of "CSKS: Continuously Steering LLMs Sensitivity to Contextual Knowledge with Proxy Models" (EMNLP 2025)

[EMNLP 2023] A Causal View of Entity Bias in (Large) Language Models

Analyzing the correlation between Hallucinations and Knowledge Conflicts in Large Language Models

Accept or Repair: Preserving Passage-Supported Answers in Contrastive Decoding

Do stale facts override new ones in agent memory? Seven memory designs, four local models, every answer checked by code

Investigating intra-memory knowledge conflicts emergence in BigBird

Official code and dataset for "Contextual Knowledge Conflicts in RAG Systems with a Recipe Dataset" (AIxIA 2026). A framework for evaluating how large language models respond to conflicting recipe ingredient variants, comparing generation with oracle context against generation without context.

RetrievalStress: a benchmark of how LLMs handle retrieved context that conflicts with their knowledge, distracts, or spans multiple hops

RIVER is a recency-invariant, validity-sensitive evidence-revision framework for reliable multi-turn retrieval-augmented generation.