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From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models
Large language models rely on prompt context to perform in-context learning, follow formats, and adapt to local evidence. This ability, however, also raises a robustness concern: models may reuse irrelevant tokens from earlier context when answering a later query. Prior work identifies this behavior
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