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In the era of vast digital information, the sheer volume and heterogeneity of available information present significant challenges for intricate information seeking.
A formal basis for the heuristic determination of minimum cost paths
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Mastering the game of Go with deep neural networks and tree search
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . 2369–2380
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Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps. In Proceedings of the 28th International Conference on Computational Linguistics . 6609–6625
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Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 6769–6781
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
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Webgpt: Browser-assisted question-answering with human feedback
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Mastering Atari Games with Limited Data. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual . 25476–25488
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MuSiQue: Multi-hop Questions via Single-hop Question Composition
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Chain-of-thought prompting elicits reasoning in large language models
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Monte Carlo Tree Search: a review of recent modifications and applications
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Decomposed Prompting: A Modular Approach for Solving Complex Tasks. In The Eleventh International Conference on Learning Representations
Zero-Shot Multi-Hop Question Answering via Monte-Carlo Tree Search with Large Language Models
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Let’s Verify Step by Step. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net
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Improve Mathematical Reasoning in Language Models by Automated Process Supervision
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Agent q: Advanced reasoning and learning for autonomous ai agents
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Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2023 · 2023
Cited alongside, same era.
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 10014–10037
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2023 · 2023
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Query2doc: Query Expansion with Large Language Models. In The 2023 Conference on Empirical Methods in Natural Language Processing
Liang Wang, Nan Yang, and Furu Wei. 2023 · 2023
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ReAct: Synergizing Reasoning and Acting in Language Models. In International Conference on Learning Representations (ICLR)
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023 · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. In The Twelfth International Conference on Learning Representations
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024 · 2024
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AlphaMath Almost Zero: process Supervision without process
Guoxin Chen, Minpeng Liao, Chengxi Li, and Kai Fan. 2024a · 2024
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Mindsearch: Mimicking human minds elicits deep ai searcher
Zehui Chen, Kuikun Liu, Qiuchen Wang, Jiangning Liu, Wenwei Zhang, Kai Chen, and Feng Zhao. 2024b · 2024
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Infogent: An agent-based framework for web information aggregation
Revanth Gangi Reddy, Sagnik Mukherjee, Jeonghwan Kim, Zhenhailong Wang, Dilek Hakkani-Tur, and Heng Ji. 2024 · 2024
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Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning
Amrith Setlur, Chirag Nagpal, Adam Fisch, Xinyang Geng, Jacob Eisenstein, Rishabh Agarwal, Alekh Agarwal, Jonathan Berant, and Aviral Kumar. 2024 · 2024
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2024 · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. 2024 · 2024
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Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning
Chaojie Wang, Yanchen Deng, Zhiyi Lv, Zeng Liang, Jujie He, Shuicheng Yan, and Bo An. 2024a · 2024
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Q*: Improving multi-step reasoning for llms with deliberative planning
Chaojie Wang, Yanchen Deng, Zhiyi Lyu, Liang Zeng, Jujie He, Shuicheng Yan, and Bo An. 2024b · 2024
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Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks. In Proceedings of the ACM on Web Conference 2024 . 1362–1373
Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng, and Tat-Seng Chua. 2024 · 2024
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Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, and Noah D. Goodman. 2024 · 2024
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LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning
Di Zhang, Jianbo Wu, Jingdi Lei, Tong Che, Jiatong Li, Tong Xie, Xiaoshui Huang, Shufei Zhang, Marco Pavone, Yuqiang Li, et al · 2024
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Rest-mcts*: Llm self-training via process reward guided tree search
Dan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue, Yuxiao Dong, and Jie Tang. 2024b · 2024
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FanOutQA: A multi-hop, multi-document question answering benchmark for large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . 18–37
Andrew Zhu, Alyssa Hwang, Liam Dugan, and Chris Callison-Burch. 2024 · 2024
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