Beyond Semantic Similarity: Reducing Unnecessary API Calls via Behavior-Aligned Retriever
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915459371106304 |
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| author | Chen, Yixin Xiong, Ying Wu, Shangyu Cui, Yufei Liu, Xue Guan, Nan Xue, Chun Jason |
| author_facet | Chen, Yixin Xiong, Ying Wu, Shangyu Cui, Yufei Liu, Xue Guan, Nan Xue, Chun Jason |
| contents | Tool-augmented large language models (LLMs) leverage external functions to extend their capabilities, but inaccurate function calls can lead to inefficiencies and increased costs.Existing methods address this challenge by fine-tuning LLMs or using demonstration-based prompting, yet they often suffer from high training overhead and fail to account for inconsistent demonstration samples, which misguide the model's invocation behavior. In this paper, we trained a behavior-aligned retriever (BAR), which provides behaviorally consistent demonstrations to help LLMs make more accurate tool-using decisions. To train the BAR, we construct a corpus including different function-calling behaviors, i.e., calling or non-calling.We use the contrastive learning framework to train the BAR with customized positive/negative pairs and a dual-negative contrastive loss, ensuring robust retrieval of behaviorally consistent examples.Experiments demonstrate that our approach significantly reduces erroneous function calls while maintaining high task performance, offering a cost-effective and efficient solution for tool-augmented LLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14323 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Semantic Similarity: Reducing Unnecessary API Calls via Behavior-Aligned Retriever Chen, Yixin Xiong, Ying Wu, Shangyu Cui, Yufei Liu, Xue Guan, Nan Xue, Chun Jason Computation and Language Tool-augmented large language models (LLMs) leverage external functions to extend their capabilities, but inaccurate function calls can lead to inefficiencies and increased costs.Existing methods address this challenge by fine-tuning LLMs or using demonstration-based prompting, yet they often suffer from high training overhead and fail to account for inconsistent demonstration samples, which misguide the model's invocation behavior. In this paper, we trained a behavior-aligned retriever (BAR), which provides behaviorally consistent demonstrations to help LLMs make more accurate tool-using decisions. To train the BAR, we construct a corpus including different function-calling behaviors, i.e., calling or non-calling.We use the contrastive learning framework to train the BAR with customized positive/negative pairs and a dual-negative contrastive loss, ensuring robust retrieval of behaviorally consistent examples.Experiments demonstrate that our approach significantly reduces erroneous function calls while maintaining high task performance, offering a cost-effective and efficient solution for tool-augmented LLMs. |
| title | Beyond Semantic Similarity: Reducing Unnecessary API Calls via Behavior-Aligned Retriever |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.14323 |