推理架构的比较分析

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参考文献

ReAct: Synergizing Reasoning and Acting in Language Models, Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao, 2023 arXiv preprint arXiv:2210.03629 DOI: 10.48550/arXiv.2210.03629 - 介绍了ReAct框架，将推理（思考）和行动（动作）步骤交织在一起，使LLM能够进行动态推理以创建、维护和调整行动计划，并与外部环境交互。
Tree of Thoughts: Deliberate Problem Solving with Large Language Models, Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan, 2023 Advances in Neural Information Processing Systems (NeurIPS) 36 DOI: 10.48550/arXiv.2305.10601 - 提出了Tree of Thoughts框架，通过允许LLM探索多个推理路径并评估中间思考来推广思维链提示，从而在复杂任务中实现更强大的问题解决能力。
Graph of Thoughts: Towards Large Language Model Based Cognitive Architectures, Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, Torsten Hoefler, 2024 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38 (Association for the Advancement of Artificial Intelligence (AAAI)) DOI: 10.1609/aaai.v38i16.29720 - 将Tree of Thoughts概念扩展到更通用的图结构，使LLM能够更灵活地管理和操作复杂的思维过程，支持高级推理和规划。
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela, 2020 Advances in Neural Information Processing Systems (NeurIPS) 33, Vol. 33 DOI: 10.48550/arXiv.2005.11401 - 介绍了检索增强生成（RAG），这是一种通过将生成过程建立在检索到的文档上，从而提高LLM事实准确性并减少幻觉的基本方法，与“自我提问”方法的事实基础目标高度相关。