BC协议：结构化双专家对话用于生成高质量思维链后训练数据
BC Protocol: Structured Dual-Expert Dialogue for Eliciting High-Quality Chain-of-Thought Post-Training Data
AI总结：
针对大语言模型后训练中高质量专家思维链数据生产瓶颈，提出BC协议——一种结构化双专家引出方法，通过配对领域专家与知识工程师，系统外化专家隐性判断为自然语言推理链，实验证明其在推理过程自然性上具有压倒性优势。
摘要：
高质量的专家思维链（CoT）数据是大语言模型（LLM）后训练的核心瓶颈之一。现有数据生产方法各有结构性局限：众包标注缺乏深度推理路径；专家单独写作受限于“专家盲点”——专家会结构性跳过他们认为显而易见的推理步骤；RLHF仅产生偏好信号而非推理链。 本文提出BC协议——一种用于LLM后训练数据生产的结构化双专家引出方法。该方法精心配对领域专家（晶体智力）与知识工程师（流体智力），系统地将专家的隐性判断外化为自然语言推理链。我们引入了参与者资质模型，定义了影响引出质量的六个参与者特征维度。“校准的无知”是本文提出的原创概念。我们进一步提出“选择优于规定”作为方法论原则：对于隐性知识引出任务，将质量控制资源投入人员选择比投入同等资源于流程设计能获得更高回报。 在叙事小说领域的受控实验中，我们直接比较了BC协议双对话产生的CoT（A组，n=20）与同一领域专家独立撰写的CoT（B组，n=20）。三个跨供应商评判模型——GPT-4o、Claude Opus 4.5和Gemini 2.5 Pro——在五个维度上进行了盲评（共600个评分）。结果表明，BC协议在“推理过程自然性”上具有压倒性优势（A组均值4.80 vs. B组均值1.30，p=2.4×10^{-8}，Cliff's δ=1.0）。
英文摘要：
High-quality expert chain-of-thought (CoT) data is one of the core bottlenecks in large language model (LLM) post-training. Existing data production methods each have structural limitations: crowdsourced annotation lacks deep reasoning paths; expert solo writing is constrained by the "expert blind spot" -- experts structurally skip reasoning steps they consider obvious; RLHF only produces preference signals rather than reasoning chains. This paper proposes the BC Protocol -- a structured dual-expert elicitation method for LLM post-training data production. The method carefully pairs a domain expert (crystallized intelligence) with a knowledge engineer (fluid intelligence), systematically externalizing the expert's implicit judgments as natural language reasoning chains. We introduce the Participant Aptitude Model, which defines six participant characteristic dimensions that affect elicitation quality. "Calibrated Ignorance" is an original concept proposed in this paper. We further propose "Selection-over-Prescription" as a methodological principle: for implicit knowledge elicitation tasks, investing quality-control resources in personnel selection yields a higher return than investing the same resources in process design. In a controlled experiment in the narrative fiction domain, we directly compared CoT produced by BC Protocol dual dialogue (Group A, (n=20)) against CoT written independently by the same domain expert (Group B, (n=20)). Three cross-vendor judge models -- GPT-4o, Claude Opus 4.5, and Gemini 2.5 Pro -- conducted blind evaluation across five dimensions (600 ratings total). Results show that the BC Protocol achieves an overwhelming advantage in "naturalness of reasoning process" (Group A mean 4.80 vs. Group B mean 1.30, (p=2.4\times10^{-8}), Cliff's (δ=1.0)).
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。
