嘿，GPT-OSS，看来你已经掌握了——现在让我带你走一遍！对数字取证中推理语言模型链式思维机制的评估
Hey GPT-OSS, Looks Like You Got It -- Now Walk Me Through It! An Assessment of the Reasoning Language Models Chain of Thought Mechanism for Digital Forensics
AI总结：
本文评估了推理语言模型在数字取证中的应用，发现中等推理水平有助于解释模型输出，但更高推理水平未显著提升响应质量。
摘要：
大型语言模型在数字取证中的应用已被广泛研究。除了识别潜在的应用，研究还聚焦于通过微调优化模型性能以适应取证任务。然而，有限的结果可解释性降低了其操作和法律实用性。最近，一种新的推理语言模型类出现，旨在通过内部推理机制处理基于逻辑的任务。然而，用户通常只能看到最终答案，而看不到背后的推理过程。其中一种推理模型是gpt-oss，它可以在本地部署，提供对底层推理过程的完整访问。本文首次探讨推理语言模型在数字取证中的潜力。四个测试用例被检查以评估推理组件在支持结果可解释性方面的实用性。评估结合了新的定量指标和定性分析。发现显示，在中等推理水平上，推理组件有助于解释和验证语言模型在数字取证中的输出，但这种支持往往有限，而更高推理水平并未提高响应质量。
英文摘要：
The use of large language models in digital forensics has been widely explored. Beyond identifying potential applications, research has also focused on optimizing model performance for forensic tasks through fine-tuning. However, limited result explainability reduces their operational and legal usability. Recently, a new class of reasoning language models has emerged, designed to handle logic-based tasks through an `internal reasoning' mechanism. Yet, users typically see only the final answer, not the underlying reasoning. One of these reasoning models is gpt-oss, which can be deployed locally, providing full access to its underlying reasoning process. This article presents the first investigation into the potential of reasoning language models for digital forensics. Four test use cases are examined to assess the usability of the reasoning component in supporting result explainability. The evaluation combines a new quantitative metric with qualitative analysis. Findings show that the reasoning component aids in explaining and validating language model outputs in digital forensics at medium reasoning levels, but this support is often limited, and higher reasoning levels do not enhance response quality.
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。
