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 force models into a binary choice between context and memory, leading to unreliable predictions.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 the model acts as a judge to evaluate 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. 1

Weize Liu, Guocong Li, Kai Zhang, Bang Du, Qiyuan Chen, Xuming Hu, Hongxia Xu, Jintai Chen, Jian Wu. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.