任务相关性：知识需求影响LLM在上下文记忆冲突中的响应
Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict
AI总结：
研究探讨了任务类型如何影响LLM在上下文与记忆冲突中的表现，发现任务对知识的依赖和冲突合理性共同影响性能，提出任务感知方法以平衡上下文与记忆。
摘要：
大型语言模型（LLM）同时依赖上下文信息和参数记忆，但这两者可能产生冲突。先前研究主要集中在上下文问答任务，隐含假设任务应依赖提供的上下文，但不清楚当任务要求不同类型的知识利用时LLM的行为。本文提出一个模型无关的诊断框架，在保持底层知识不变的情况下，引入不同知识需求任务间的可控冲突。实验表明，冲突下的性能下降由任务特定的知识依赖和冲突合理性共同驱动；策略如理由或上下文重述增加上下文依赖，帮助仅依赖上下文的任务但损害需要参数知识的任务；这些影响偏转基于模型的评估，质疑LLM作为评判者的可靠性。总体而言，研究发现上下文-记忆冲突本质上是任务依赖的，推动任务感知方法以平衡上下文与记忆在LLM部署和评估中的应用。
英文摘要：
Large language models (LLMs) draw on both contextual information and parametric memory, yet these sources can conflict. Prior studies have largely examined this issue in contextual question answering, implicitly assuming that tasks should rely on the provided context, leaving unclear how LLMs behave when tasks require different types and degrees of knowledge utilization. We address this gap with a model-agnostic diagnostic framework that holds underlying knowledge constant while introducing controlled conflicts across tasks with varying knowledge demands. Experiments on representative open-weight and proprietary LLMs show that performance degradation under conflict is driven by both task-specific knowledge reliance and conflict plausibility; that strategies such as rationales or context reiteration increase context reliance, helping context-only tasks but harming those requiring parametric knowledge; and that these effects bias model-based evaluation, calling into question the reliability of LLMs as judges. Overall, our findings reveal that context-memory conflict is inherently task-dependent and motivate task-aware approaches to balancing context and memory in LLM deployment and evaluation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
