CAPA基准：跨会话个性化歧义适应提升编程助手效率
原标题：Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants
AI 摘要
Hugging Face 每日论文介绍了一项关于编程助手个性化歧义适应的新研究。研究者提出了 CAPA 基准，通过六种机制刻画个性化编程歧义，并利用受控三阶段生成流程将其注入可执行任务，构建了包含 600 个编程会话的数据集。研究评估了 12 个近期大语言模型在无历史和有同用户历史条件下的表现，并提出了一种轻量级的同用户历史门控方法，以减少澄清次数并提升代码与用户意图的一致性。
正文节选
Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants Abstract AI-assisted coding increasingly translates informal user intent into executable software, yet coding requests often contain ambiguities that recur in user-specific ways across tasks and sessions. Existing disambiguation methods typically address each ambiguous request in isolation within the current coding session, often through eliciting additional clarification. However