函数调用中结构化输出增益的归因：接口对齐与过程转移
Attributing Structured-Output Gains in Function Calling: Interface Alignment versus Procedural Transfer
浏览论文内容
中文总结 AI 辅助
研究结构化输出基准中提示诱导函数调用增益的归因，介绍四层增益归因协议，应用于相关数据集得出部分增益源于接口对齐而非过程转移的结论，并发布相关资源。
中文摘要 AI 辅助
结构化输出基准奖励任务决策和接口合规性，提示诱导的函数调用增益需归因才能解释为可转移技能。我们为提示前置技能注入引入四层增益归因协议，应用于相关数据集表明部分增益应归因于接口对齐而非过程转移，还发布了相关资源。
英文摘要
Structured-output benchmarks reward both task decisions and interface compliance, so prompt-induced function-calling gains require attribution before they can be interpreted as transferable skill. We introduce a four-layer gain-attribution protocol for prompt-prepended skill injection, combining canonicalized rescoring, format-only controls, repaired/balanced induction, and portability checks. Applied to the Berkeley Function Calling Leaderboard (BFCL) and scoped with API-Bank, MATH-500, and MultiHop-RAG, the protocol shows that several apparent gains are better attributed to interface alignment than to procedural transfer: format-only prompts match or exceed full skills in key BFCL cells, repaired/balanced induction removes the largest sub-frontier gains, and API-Bank target-native gains are matched within 0.5 percentage points (pp) by length-matched generic procedural prompts. These findings treat format compliance as a useful engineering capability while clarifying what a structured-output score certifies. We release BFCL-CANONICAL and recommend canonicalized metrics, balanced induction, and format-only baselines for function-calling skill-gain attribution. Code and data are available at https://github.com/couragec/skill-injection-attribution.
