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We introduce MCTS-RAG, a novel approach that enhances the reasoning capabilities of small language models on knowledge-intensive tasks by leveraging retrieval-augmented generation (RAG) to provide relevant context and Monte Carlo Tree Search (MCTS) to refine reasoning paths.
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Jiatao Li, Xinyu Hu, and Xiaojun Wan. 2024 · 2024
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Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2023 · 2023
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023 · 2023
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Self-RAG: Learning to retrieve, generate, and critique through self-reflection
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024 · 2024
Cited alongside, same era.
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Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024 · 2024
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Open-rag: Enhanced retrieval augmented reasoning with open-source large language models
Shayekh Islam, Md Asib Rahman, KSM Tozammel Hossain, Enamul Hoque, Shafiq Joty, and Md Rizwan Parvez. 2024 · 2024
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Tianyu Fan, Jingyuan Wang, Xubin Ren, and Chao Huang. 2025 · 2025
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