@inproceedings{chen-etal-2026-attributing,
title = "Attributing Structured-Output Gains in Function Calling: Interface Alignment versus Procedural Transfer",
author = "Chen, Wanyi and
Chen, Daoyuan and
Kong, Fang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.764/",
pages = "16119--16136",
ISBN = "979-8-89176-462-0",
abstract = "Function-calling benchmarks assess whether language models can select appropriate tools and supply the required arguments. Because their scores also depend on expressing calls in an evaluator-specific format, gains from prompt-prepended skills may reflect interface compliance rather than transferable task-solving procedures. 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 is available at https://github.com/wanyichen06/skill-injection-attribution."
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<abstract>Function-calling benchmarks assess whether language models can select appropriate tools and supply the required arguments. Because their scores also depend on expressing calls in an evaluator-specific format, gains from prompt-prepended skills may reflect interface compliance rather than transferable task-solving procedures. 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 is available at https://github.com/wanyichen06/skill-injection-attribution.</abstract>
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%0 Conference Proceedings
%T Attributing Structured-Output Gains in Function Calling: Interface Alignment versus Procedural Transfer
%A Chen, Wanyi
%A Chen, Daoyuan
%A Kong, Fang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-attributing
%X Function-calling benchmarks assess whether language models can select appropriate tools and supply the required arguments. Because their scores also depend on expressing calls in an evaluator-specific format, gains from prompt-prepended skills may reflect interface compliance rather than transferable task-solving procedures. 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 is available at https://github.com/wanyichen06/skill-injection-attribution.
%U https://aclanthology.org/2026.findings-emnlp.764/
%P 16119-16136
Markdown (Informal)
[Attributing Structured-Output Gains in Function Calling: Interface Alignment versus Procedural Transfer](https://aclanthology.org/2026.findings-emnlp.764/) (Chen et al., Findings 2026)
ACL