@inproceedings{li-etal-2026-debate,
    title = "Debate-of-Thoughts: Resolving Knowledge Conflicts in {LLM}s Through Internal Deliberation",
    author = "Li, Guocong  and
      Hu, Qirui  and
      Wang, Ping  and
      Zhang, Guofeng  and
      Wu, Jian  and
      Xu, Hongxia",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1651/",
    pages = "35674--35696",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models enhanced with Retrieval Augmented Generation show strong potential in knowledge intensive tasks. However, they often encounter knowledge conflicts, where retrieved information contradicts the model{'}s internal knowledge or exhibits internal inconsistencies. Existing methods treat this as a simplistic binary choice, forcing models to blindly trust external contexts or rigidly rely on memory, resulting in unreliable predictions that swing between sycophancy and stubbornness. We argue that a more principled approach is to embrace contradictions as opportunities for deeper reasoning. To this end, we introduce Debate-of-Thoughts (DoT), a framework that transforms conflict resolution into an active deliberation process. DoT guides a single model through three phases: 1) hypothesis generation, which forms competing perspectives; 2) internal debate, where the model acts as both a proponent and a critic to stress test each view; and 3) adjudication, where a judge module evaluates arguments based on evidence and logical consistency. We implement DoT via two complementary strategies: inference time prompt chaining and supervised fine tuning. Experiments across multiple conflict benchmarks show that DoT consistently outperforms state-of-the-art methods, while generating transparent debate transcripts that explain its decisions. By improving both accuracy and interpretability under knowledge conflicts, DoT establishes a more reliable paradigm for retrieval augmented generation systems."
}