Year: 2025
Type: conference-paper
Abstract: 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. Instruction Input ExampleRewrite Reasoning Instruction: Paraphrase the following text.Input: Laundry detergents were once manufactured to contain high ... which would a lake become as a result of the phosphorous in the detergent?Options : A. canyon B. desert C. swamp D. river
Institutions: Seoul National University, LG AI Research (South Korea)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412944872