Chengrui Huang — accepted AI-conference papers

2026

What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks

Chengrui Huang, Zhengliang Shi, Yuntao Wen, Xiuying Chen, Peng Han, Shen Gao +1

IJCAI 2026

Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications. Many existing works fine-tune LLMs or design prompts to enable LLMs to select appropriate tools and correctly invoke them to meet user requirements. However, it is observed in pr

Cited by 0 Scholar ↗

2025

TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation

Chengrui Huang, Shen Gao, Zhengliang Shi, Dongsheng Wang, Shuo Shang

EMNLP 2025

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**refe

Cited by 0 Source ↗PDF ↗Scholar ↗