@inproceedings{wang-etal-2025-inspiredebate,
    title = "{I}nspire{D}ebate: Multi-Dimensional Subjective-Objective Evaluation-Guided Reasoning and Optimization for Debating",
    author = "Wang, Fuyu  and
      Li, Jiangtong  and
      Zhu, Kun  and
      Jiang, Changjun",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.acl-long.1335/",
    doi = "10.18653/v1/2025.acl-long.1335",
    pages = "27525--27544",
    ISBN = "979-8-89176-251-0",
    abstract = "With the rapid advancements in large language models (LLMs), debating tasks, such as argument quality assessment and debate process simulation, have made significant progress. However, existing LLM-based debating systems focus on responding to specific arguments while neglecting objective assessments such as authenticity and logical validity. Furthermore, these systems lack a structured approach to optimize across various dimensions{---}including evaluation metrics, chain-of-thought (CoT) reasoning, and multi-turn debate refinement{---}thereby limiting their effectiveness. To address these interconnected challenges, we propose a dual-component framework: (1) InspireScore, a novel evaluation system that establishes a multi-dimensional assessment architecture incorporating four subjective criteria (emotional appeal, argument clarity, argument arrangement, and topic relevance) alongside two objective metrics (fact authenticity and logical validity); and (2) InspireDebate, an optimized debating framework employing a phased optimization approach through CoT reasoning enhancement, multi-dimensional Direct Preference Optimization (DPO), and real-time knowledge grounding via web-based Retrieval Augmented Generation (Web-RAG). Empirical evaluations demonstrate that InspireScore achieves 44{\%} higher correlation with expert judgments compared to existing methods, while InspireDebate shows significant improvements, outperforming baseline models by 57{\%}. Source code is available at https://github.com/fywang12/InspireDebate."
}