ProactBench: 超出用户所要求的
ProactBench: Beyond What The User Asked For
AI总结：
ProactBench研究对话主动性，通过三个阶段分解能力，设计代理对抗偏见，验证不同模型在恢复阶段的表现。
摘要：
大多数LLM基准测试评估模型对显式请求的响应能力，但未测量另一种对话能力：识别并响应用户未明说的需要。我们称之为对话主动性。ProactBench将其分解为三个阶段相关的类型：Emergent（从单个披露锚点推断）、Critical（跨多个锚点综合）和Recovery（任务完成后基于未来视角的价值）。我们通过三个代理：规划者、用户代理和助手模型来操作该基准测试。信息不对称性防止了风格混淆评分、规则泄露、外部上下文污染和信息溢出。发布的语料库包含198个经过筛选的对话，其中624个触发点涵盖24种来自心理测量库存的交流风格，并由独立LLM法官审核。在16个前沿和开放权重模型上，Recovery既困难又弱预测，使其成为有用的新的评估信号。
英文摘要：
Most LLM benchmarks score how well a model responds to explicit requests. They leave unmeasured a different conversational ability: noticing and acting on needs the user has implied but not said. We call this \emph{conversational proactivity}. ProactBench decomposes it into three phase-tied types: \textsc{Emergent}, inference from a single disclosed anchor; \textsc{Critical}, synthesis across multiple anchors; and \textsc{Recovery}, grounded forward-looking value after task completion. We operationalise the benchmark with three agents: a Planner, a User Agent, and an Assistant Model. Their information asymmetries defend against style-confounded scoring, rubric leakage, external-context contamination, and information dumps. The released corpus contains 198 curated dialogues with 624 trigger points across 24 communication styles drawn from a psychometric inventory and audited by an independent LLM judge. Across 16 frontier and open-weight models, \textsc{Recovery} is both difficult and weakly predicted by six standard benchmarks, making it a useful new evaluation signal.
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。
