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Retrieval-Augmented Generation (RAG) systems for Large Language Models (LLMs) hold promise in knowledge-intensive tasks but face limitations in complex multi-step reasoning.
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FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
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ReFT: Reasoning with Reinforced Fine-Tuning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Computational Linguistics, Bangkok, Thailand, 7601–7614
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Enhancing Mathematical Reasoning in LLMs by Stepwise Correction
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Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning
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RECOMP: Improving Retrieval-Augmented LMs with Context Compression and Selective Augmentation. In The Twelfth International Conference on Learning Representations
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Marco-o1: Towards open reasoning models for open-ended solutions
Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang, Tianqi Shi, Chenyang Lyu, Longyue Wang, Weihua Luo, and Kaifu Zhang. 2024 · 2024
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