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LLM_Reasoning_Papers

Papers on LLM Reasoning, mainly contributed and maintained by Jinxin Liu and Amy Xin.

LLM Reasoning Topologies:

  1. Chain-of-thought prompting elicits reasoning in large language models Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou. (NeurIPS 2022) [paper]

  2. Tree of Thoughts: Deliberate Problem Solving with Large Language Models Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, Karthik Narasimhan. (NeurIPS 2023) [paper]

  3. Graph of Thoughts: Solving Elaborate Problems with Large Language Models Authors Maciej Besta, Nils Blach, Ales Kubicek1, Robert Gerstenberger, Michał Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann3, Hubert Niewiadomski3, Piotr Nyczyk3, Torsten Hoefler. (AAAI 2024) [paper]

  4. Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding Mirac Suzgun, Adam Tauman Kalai [paper]

  5. Cumulative Reasoning with Large Language Models Yifan Zhang, Jingqin Yang, Yang Yuan, Andrew Chi-Chih Yao [paper]

Retrieval-Augmented LLM Reasoning:

  1. ReAct: Synergizing Reasoning and Acting in Language Models Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao. (ICLR 2023) [paper]

  2. Self-Ask: Measuring and Narrowing the Compositionality Gap in Language Models Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, Mike Lewis (EMNLP 2023) [paper]

  3. IRCoT: Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal (ACL 2023) [paper]

  4. ITER-RETGEN: Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, Weizhu Chen (EMNLP 2023) [paper]

  5. ProbTree: Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions Shulin Cao, Jiajie Zhang, Jiaxin Shi, Xin Lv, Zijun Yao, Qi Tian, Juanzi Li, Lei Hou (EMNLP 2023) [paper]

  6. Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, Lidong Bing (ACL 2023) [paper]

  7. Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, Matei Zaharia [paper]

  8. MCR: Answering Questions by Meta-Reasoning over Multiple Chains of Thought Ori Yoran, Tomer Wolfson, Ben Bogin, Uri Katz, Daniel Deutch, Jonathan Berant (EMNLP 2023) [paper]

  9. FLARE: Active Retrieval Augmented Generation Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, Graham Neubig (EMNLP 2023) [paper]

  10. Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources Xingxuan Li, Ruochen Zhao, Yew Ken Chia, Bosheng Ding, Shafiq Joty, Soujanya Poria, Lidong Bing (ICLR 2024) [paper]

  11. Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng, Tat-Seng Chua (WWW 2023) [paper]

  12. BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering Zheng Chu, Jingchang Chen, Qianglong Chen, Haotian Wang, Kun Zhu, Xiyuan Du, Weijiang Yu, Ming Liu, Bing Qin (ACL2024) [paper]