面向大语言模型中透明思维链推理的政策框架
Policy Frameworks for Transparent Chain-of-Thought Reasoning in Large Language Models
AI总结：
针对当前大语言模型CoT披露政策不统一的问题，本文分析了CoT全面披露的双重影响，提出分级访问政策框架，通过分级权限、伦理许可等平衡透明、问责与安全，推动负责任AI部署。
摘要：
思维链（Chain-of-Thought, CoT）推理通过将复杂问题分解为逐步求解方案来增强大语言模型（LLMs），提升其在推理任务上的表现。然而，当前不同模型的CoT披露政策在前端可见性、API访问权限和定价策略方面差异极大，缺乏统一的政策框架。本文分析了全面披露CoT的双重影响：它既能助力小模型蒸馏、增进信任并支持错误诊断，也存在侵犯知识产权、被滥用以及产生运营成本的风险。我们提出了一种分级访问政策框架，通过伦理许可、结构化推理输出和跨层级保障机制，为学术用户、商业用户和普通用户定制不同的CoT获取权限，以此平衡透明度、问责制与安全性。该框架通过协调可访问性与伦理及运营层面的考量，旨在推动负责任的AI部署，同时降低滥用或误解的风险。
英文摘要：
Chain-of-Thought (CoT) reasoning enhances large language models (LLMs) by decomposing complex problems into step-by-step solutions, improving performance on reasoning tasks. However, current CoT disclosure policies vary widely across different models in frontend visibility, API access, and pricing strategies, lacking a unified policy framework. This paper analyzes the dual-edged implications of full CoT disclosure: while it empowers small-model distillation, fosters trust, and enables error diagnosis, it also risks violating intellectual property, enabling misuse, and incurring operational costs. We propose a tiered-access policy framework that balances transparency, accountability, and security by tailoring CoT availability to academic, business, and general users through ethical licensing, structured reasoning outputs, and cross-tier safeguards. By harmonizing accessibility with ethical and operational considerations, this framework aims to advance responsible AI deployment while mitigating risks of misuse or misinterpretation.
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。
