@inproceedings{huang-etal-2025-ttpa,
title = "{TTPA}: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation",
author = "Huang, Chengrui and
Gao, Shen and
Shi, Zhengliang and
Wang, Dongsheng and
Shang, Shuo",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.882/",
pages = "16240--16255",
ISBN = "979-8-89176-335-7",
abstract = "Existing tool-learning methods usually rely on supervised fine-tuning, they often overlook fine-grained optimization of internal tool call details, leading to limitations in preference alignment and error discrimination. To overcome these challenges, we propose **T**oken-level **T**ool-use **P**reference **A**lignment Training Framework (TTPA), a training paradigm for constructing token-level tool-use preference datasets that align LLMs with fine-grained preferences using a novel error-oriented scoring mechanism. TTPA first introduces reversed dataset construction, a method for creating high-quality, multi-turn tool-use datasets by reversing the generation flow. Additionally, we propose {\_}Preference Oriented Tool-use Dataset Construction{\_} to capture fine-grained preferences by modeling token-level differences during generation. To address biases in scoring, we introduce the {\_}Error-oriented Scoring Mechanism{\_}, which quantifies tool-call errors and can be used as a training signal. Extensive experiments on three diverse benchmark datasets demonstrate that TTPA significantly improves tool-using performance while showing strong generalization ability across models and datasets."
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<abstract>Existing tool-learning methods usually rely on supervised fine-tuning, they often overlook fine-grained optimization of internal tool call details, leading to limitations in preference alignment and error discrimination. To overcome these challenges, we propose **T**oken-level **T**ool-use **P**reference **A**lignment Training Framework (TTPA), a training paradigm for constructing token-level tool-use preference datasets that align LLMs with fine-grained preferences using a novel error-oriented scoring mechanism. TTPA first introduces reversed dataset construction, a method for creating high-quality, multi-turn tool-use datasets by reversing the generation flow. Additionally, we propose _Preference Oriented Tool-use Dataset Construction_ to capture fine-grained preferences by modeling token-level differences during generation. To address biases in scoring, we introduce the _Error-oriented Scoring Mechanism_, which quantifies tool-call errors and can be used as a training signal. Extensive experiments on three diverse benchmark datasets demonstrate that TTPA significantly improves tool-using performance while showing strong generalization ability across models and datasets.</abstract>
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%0 Conference Proceedings
%T TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation
%A Huang, Chengrui
%A Gao, Shen
%A Shi, Zhengliang
%A Wang, Dongsheng
%A Shang, Shuo
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F huang-etal-2025-ttpa
%X Existing tool-learning methods usually rely on supervised fine-tuning, they often overlook fine-grained optimization of internal tool call details, leading to limitations in preference alignment and error discrimination. To overcome these challenges, we propose **T**oken-level **T**ool-use **P**reference **A**lignment Training Framework (TTPA), a training paradigm for constructing token-level tool-use preference datasets that align LLMs with fine-grained preferences using a novel error-oriented scoring mechanism. TTPA first introduces reversed dataset construction, a method for creating high-quality, multi-turn tool-use datasets by reversing the generation flow. Additionally, we propose _Preference Oriented Tool-use Dataset Construction_ to capture fine-grained preferences by modeling token-level differences during generation. To address biases in scoring, we introduce the _Error-oriented Scoring Mechanism_, which quantifies tool-call errors and can be used as a training signal. Extensive experiments on three diverse benchmark datasets demonstrate that TTPA significantly improves tool-using performance while showing strong generalization ability across models and datasets.
%U https://aclanthology.org/2025.findings-emnlp.882/
%P 16240-16255
Markdown (Informal)
[TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation](https://aclanthology.org/2025.findings-emnlp.882/) (Huang et al., Findings 2025)
ACL