@inproceedings{sun-etal-2026-task,
    title = "Task Matters: Knowledge Requirements Shape {LLM} Responses to Context{--}Memory Conflict",
    author = "Sun, Kaiser  and
      Bai, Fan  and
      Dredze, Mark",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.202/",
    pages = "4154--4176",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) rely on both contextual knowledge and parametric memory, yet these sources can conflict. Prior analysis largely focused on contextual question answering, suggesting that models tend to favor parametric knowledge under conflict, but this setting assumes that tasks should always rely on the provided passage. It therefore remains unclear how LLMs behave when \textit{tasks demand different kinds and degrees of knowledge utilization}. We address this gap with a model-agnostic diagnostic framework that holds underlying knowledge constant while injecting controlled conflicts across tasks with varying knowledge requirements. Evaluating representative open-source LLMs, we find that: (1) performance degradation under conflict correlates with a task{'}s knowledge reliance rather than conflict plausibility alone; (2) strategies such as explanatory rationales or reiteration increase context reliance, helping context-only tasks but harming those that require parametric knowledge; and (3) these behaviors bias model-based evaluation, raising concerns about the reliability of LLMs as judges. Together, our findings show that context{--}memory conflict is fundamentally task-dependent and motivate task-aware approaches to balancing context and memory in LLM deployment and evaluation."
}