PORTS: Preference-Optimized Retrievers for Tool Selection with Large Language Models
PORTS：用于大语言模型工具选择的偏好优化检索器
机构 * Department of Computer Science and Engineering, University of Bologna（计算机科学与工程系，博洛尼亚大学）
AI总结 研究针对大语言模型工具选择中现有检索器与LLMs不一致问题，提出PORTS方法，利用受困惑度启发的偏好信号，通过优化相关性及施加对比语义损失微调检索器，经多实验验证其通用性及提高工具选择准确性的能力，且计算需求低便于推广。
Comments Please cite the definitive, peer-reviewed version of this article published in the Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, edited by Christos Christodoulopoulos et al., Association for Computational Linguistics, pp. 10007-10030, 2025. DOI: https://doi.org/10.18653/v1/2025.emnlp-main.507
Journal ref Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 10007-10030, 2025
