Memory vs. Context? Influential Factors of Factual Recall in Language Models

Title:Memory vs. Context? Influential Factors of Factual Recall in Language Models

Abstract:We reproduce and stress-test the work of Yu et al. (2023), who characterize how language models (LMs) arbitrate between memorized knowledge and contradictory in-context statements. We replicate their world-capitals experiments on 31 models spanning Pythia, GPT-2, Qwen3, and Ministral families, including base and post-trained variants, and extend evaluations to five additional knowledge relation types from the ParaConflict dataset. We empirically confirm most of their original findings: larger models and higher-frequency entities tend to favor memorized answers, with substantial family-level variance. However, several conclusions do not generalize cleanly: entity-frequency effects disappear on Qwen3-14B and 32B; post-training shifts the memory-context trade-off inconsistently across families; question phrasing alone can change a model's reliance on memorized knowledge by up to 80 percentage points; and semantically unrelated prose can mimic coherent supporting context. Our results clarify where Yu et al.'s claims hold and to what extent they generalize to other prompts.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.24238 [cs.CL]

(or arXiv:2609.24238v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.24238 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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