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Tool learning has emerged as a crucial capability for large language models (LLMs) to solve complex real-world tasks through interaction with external tools.
Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2023 · 2023
Earlier work this paper cites.
Binding language models in symbolic languages
Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. 2023 · 2023
Earlier work this paper cites.
PAL: program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023 · 2023
Earlier work this paper cites.
Tool documentation enables zero-shot tool-usage with large language models
Cheng-Yu Hsieh, Si-An Chen, Chun-Liang Li, Yasuhisa Fujii, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, and Tomas Pfister. 2023 · 2023
Earlier work this paper cites.
API-bank: A comprehensive benchmark for tool-augmented LLMs
Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, and Yongbin Li. 2023 · 2023
Earlier work this paper cites.
Chameleon: Plug-and-play compositional reasoning with large language models
Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. 2023 · 2023
Earlier work this paper cites.
Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch. 2023 · 2023
Earlier work this paper cites.
ART: automatic multi-step reasoning and tool-use for large language models
Bhargavi Paranjape, Scott M. Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Túlio Ribeiro. 2023 · 2023
Earlier work this paper cites.
CREATOR: Tool creation for disentangling abstract and concrete reasoning of large language models
Cheng Qian, Chi Han, Yi Fung, Yujia Qin, Zhiyuan Liu, and Heng Ji. 2023 · 2023
Cited alongside, same era.
Reflexion: language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023
Cited alongside, same era.
Restgpt: Connecting large language models with real-world applications via restful apis
Yifan Song, Weimin Xiong, Dawei Zhu, Cheng Li, Ke Wang, Ye Tian, and Sujian Li. 2023 · 2023
Cited alongside, same era.
Toolalpaca: Generalized tool learning for language models with 3000 simulated cases
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, and Le Sun. 2023 · 2023
Cited alongside, same era.
React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R. Narasimhan, and Yuan Cao. 2023 · 2023
The landscape of emerging AI agent architectures for reasoning, planning, and tool calling: A survey
Tula Masterman, Sandi Besen, Mason Sawtell, and Alex Chao. 2024 · 2024
Later among the works it cites.
Tool learning with large language models: A survey
Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, and Ji-Rong Wen. 2024 · 2024
Later among the works it cites.
Learning to use tools via cooperative and interactive agents
Zhengliang Shi, Shen Gao, Xiuyi Chen, Yue Feng, Lingyong Yan, Haibo Shi, Dawei Yin, Pengjie Ren, Suzan Verberne, and Zhaochun Ren. 2024b · 2024
Later among the works it cites.
Executable code actions elicit better LLM agents
Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji. 2024b · 2024
Later among the works it cites.
MINT: evaluating llms in multi-turn interaction with tools and language feedback
Xingyao Wang, Zihan Wang, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng, and Heng Ji. 2024c · 2024
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Cited alongside, same era.
Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning
Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, and Yongbin Li. 2023 · 2023
Cited alongside, same era.
Chengrui Huang, Zhengliang Shi, Yuntao Wen, Xiuying Chen, Peng Han, Shen Gao, and Shuo Shang. 2024 · 2024
Cited alongside, same era.
From summary to action: Enhancing large language models for complex tasks with open world apis
Yulong Liu, Yunlong Yuan, Chunwei Wang, Jianhua Han, Yongqiang Ma, Li Zhang, Nanning Zheng, and Hang Xu. 2024 · 2024
Cited alongside, same era.
Chain of tools: Large language model is an automatic multi-tool learner
Zhengliang Shi, Shen Gao, Xiuyi Chen, Yue Feng, Lingyong Yan, Haibo Shi, Dawei Yin, Zhumin Chen, Suzan Verberne, and Zhaochun Ren. 2024a
Cited in the paper.
LLMs in the imaginarium: Tool learning through simulated trial and error
Boshi Wang, Hao Fang, Jason Eisner, Benjamin Van Durme, and Yu Su. 2024a
Cited in the paper.
EASYTOOL: enhancing llm-based agents with concise tool instruction
Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Kan Ren, Dongsheng Li, and Deqing Yang. 2024b
Cited in the paper.
Later among the works it cites.
Ke Yang, Jiateng Liu, John Wu, Chaoqi Yang, Yi R. Fung, Sha Li, Zixuan Huang, Xu Cao, Xingyao Wang, Yiquan Wang, Heng Ji, and Chengxiang Zhai. 2024 · 2024
Later among the works it cites.
CRAFT: customizing llms by creating and retrieving from specialized toolsets
Lifan Yuan, Yangyi Chen, Xingyao Wang, Yi Fung, Hao Peng, and Heng Ji. 2024a · 2024
Later among the works it cites.