在群体中思考：辅助信息如何塑造LLM推理
Thinking in a Crowd: How Auxiliary Information Shapes LLM Reasoning
AI总结：
本文针对LLMs在复杂领域应用中常依赖外部辅助信息的问题，构建SciAux数据集研究其对LLMs显式逐步推理的因果影响，发现思考模式是双刃剑：有用信息提升准确率，误导性信息则加剧错误，凸显需赋予模型信息批判能力。
摘要：
大型语言模型（LLMs）的推理能力是其在复杂、知识密集型领域应用的基础。在现实场景中，LLMs常被补充外部信息，这些信息可能有帮助、无关甚至误导性。本文研究此类辅助信息对具有显式逐步思考能力的LLMs推理过程的因果影响。我们引入SciAux数据集（基于ScienceQA构建），系统测试模型对这些信息类型的鲁棒性。研究发现关键脆弱性：模型的 deliberative“思考模式”是双刃剑。有用上下文提升准确率，而误导性信息导致性能灾难性下降，且思考过程会放大该影响。思考未增强鲁棒性，反而在提供错误信息时加剧错误程度。这凸显挑战不仅是让模型“思考”，还需赋予其评估推理所依赖信息的批判能力。SciAux数据集可在https://huggingface.co/datasets/billhdzhao/SciAux获取。
英文摘要：
The capacity of Large Language Models (LLMs) to reason is fundamental to their application in complex, knowledge-intensive domains. In real-world scenarios, LLMs are often augmented with external information that can be helpful, irrelevant, or even misleading. This paper investigates the causal impact of such auxiliary information on the reasoning process of LLMs with explicit step-by-step thinking capabilities. We introduce SciAux, a new dataset derived from ScienceQA, to systematically test the robustness of the model against these types of information. Our findings reveal a critical vulnerability: the model's deliberative "thinking mode" is a double-edged sword. While helpful context improves accuracy, misleading information causes a catastrophic drop in performance, which is amplified by the thinking process. Instead of conferring robustness, thinking reinforces the degree of error when provided with misinformation. This highlights that the challenge is not merely to make models "think", but to endow them with the critical faculty to evaluate the information upon which their reasoning is based. The SciAux dataset is available at https://huggingface.co/datasets/billhdzhao/SciAux.
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。
