LLMs很容易被指令性干扰所迷惑
LLMs can be easily Confused by Instructional Distractions
AI总结：
本文提出DIM-Bench基准，用于评估LLMs在指令性干扰下的表现，发现即使最先进的LLMs也容易受干扰而无法准确遵循用户意图。
摘要：
尽管大型语言模型（LLMs）在指令遵循任务中展现出卓越的能力，但当模型被要求忽略某些指令时，这种优势可能会转化为弱点。指令遵循任务通常包含明确的任务描述和含有待处理目标数据的输入文本。然而，当输入本身类似于指令时，即使有明确的提示来区分任务指令和输入，也可能会产生混淆。我们将这种现象称为指令性干扰。在本文中，我们引入了一个名为DIM-Bench的新颖基准，专门用于评估LLMs在指令性干扰下的表现。该基准对现实世界中的指令性干扰实例进行分类，并在四类指令任务——重写、校对、翻译和风格转换——以及五类输入任务——推理、代码生成、数学推理、偏见检测和问答——上评估LLMs。我们的实验结果表明，即使是最先进的LLMs也容易受到指令性干扰的影响，在这种情况下往往无法准确遵循用户意图。
英文摘要：
Despite the fact that large language models (LLMs) show exceptional skill in instruction following tasks, this strength can turn into a vulnerability when the models are required to disregard certain instructions. Instruction-following tasks typically involve a clear task description and input text containing the target data to be processed. However, when the input itself resembles an instruction, confusion may arise, even if there is explicit prompting to distinguish between the task instruction and the input. We refer to this phenomenon as instructional distraction. In this paper, we introduce a novel benchmark, named DIM-Bench, specifically designed to assess LLMs' performance under instructional distraction. The benchmark categorizes real-world instances of instructional distraction and evaluates LLMs across four instruction tasks: rewriting, proofreading, translation, and style transfer -- alongside five input tasks: reasoning, code generation, mathematical reasoning, bias detection, and question answering. Our experimental results reveal that even the most advanced LLMs are susceptible to instructional distraction, often failing to accurately follow user intent in such cases.
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。
