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This paper analyzes how LLMs' faithfulness to provided context depends on perceived plausibility, using factual, counterfactual, and fictional RDF triples in multiple languages. It finds a weak context–memory conflict and emphasizes that the choice of LLM judge can overestimate its strength.
This paper identifies and characterizes Decomposition-Induced Context-Memory Conflict (DI-CC), a failure mode in decompose-then-verify pipelines where decomposition substitutes the model's parametric beliefs for the source text. The authors show it is mechanistically related to classical context-memory conflict, that SelfCheckGPT fails to detect it, and that context-aware decoding suppresses it but introduces severe parsing failures.