Built on

Language models are few-shot learners
Original
Tom B. Brown, Benjamin Mann, Nicholas Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Nicholas Shinn, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
The chess transformer: Mastering play using generative language models
Original
David Noever, Matthew Ciolino, and Josh Kalin · 2020
Earlier work this paper cites.
Large language models are zero-shot reasoners
Original
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Original
Long Ouyang, Jeff Wu, Xu Jiang, Daniel Li, Srini Iyer, Eric Huang, Mark Chen, and Q. V. Le · 2022
Earlier work this paper cites.
Self-consistency improves chain of thought reasoning in language models
Original
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Original
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, Quoc V. Le, and Denny Zhou · 2022
Earlier work this paper cites.
Sparks of artificial general intelligence: Early experiments with gpt-4
Original
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, and Yin T. Levy · 2023
Earlier work this paper cites.
Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Hong, Zhen Wang, Daisy Wang, and Zhiting Hu · 2023
Earlier work this paper cites.
Large language models are reasoning teachers
Namgyu Ho, Laura Schmid, and Se-Young Yun · 2023
Earlier work this paper cites.

Similar

Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang · 2023
Cited alongside, same era.
The cot collection: Improving zero-shot and few-shot learning of language models via chain-of-thought fine-tuning
Original
Seungone Kim, Se June Joo, Doyoung Kim, Joel Jang, Seonghyeon Ye, Jamin Shin, and Minjoon Seo · 2023
Cited alongside, same era.
Llm-based agent society investigation: Collaboration and confrontation in avalon gameplay
Original
Yihuai Lan, Zhiqiang Hu, Lei Wang, Yang Wang, Deheng Ye, Peilin Zhao, Ee-Peng Lim, Hui Xiong, and Hao Wang · 2023
Cited alongside, same era.
Theory of mind for multi-agent collaboration via large language models
Huao Li, Yu Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Charles Lewis, and Katia Sycara · 2023
Cited alongside, same era.
Exploring large language models for communication games: An empirical study on werewolf
Original

Then

Reasoning with large language models, a survey
Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, and Thomas Bäck · 2024
Later among the works it cites.
Rethinking the bounds of LLM reasoning: Are multi-agent discussions the key?
Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong, and Yangqiu Song · 2024
Later among the works it cites.
Enhance reasoning for large language models in the game werewolf
Original
Shuang Wu, Liwen Zhu, Tao Yang, Shiwei Xu, Qiang Fu, Yang Wei, and Haobo Fu · 2024
Later among the works it cites.
Llms may not be human-level players, but they can be testers: Measuring game difficulty with llm agents
Original
Chang Xiao and Brenda Z. Yang · 2024
Later among the works it cites.
Deliberate reasoning for llms as structure-aware planning with accurate world model, 2024
Siheng Xiong, Ali Payani, Yuan Yang, and Faramarz Fekri · 2024
Later among the works it cites.

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2025

Exploring Large Language Models for Word Games:Who is the Spy?

Wei, Chentian, Chen, Jiewei, Xu, Jinzhu

Understand

Word games hold significant research value for natural language processing (NLP), game theory, and related fields due to their rule-based and situational nature.

This study explores how large language models (LLMs) can be effectively involved in word games and proposes a training-free framework.
"Shei Shi Wo Di" or "Who is the Spy" in English, is a classic word game.
Using this game as an example, we introduce a Chain-of-Thought (CoT)-based scheduling framework to enable LLMs to achieve excellent performance in tasks such as inferring role words and disguising their identities.

Built on

Language models are few-shot learners
Original
Tom B. Brown, Benjamin Mann, Nicholas Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Nicholas Shinn, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
The chess transformer: Mastering play using generative language models
Original
David Noever, Matthew Ciolino, and Josh Kalin · 2020
Earlier work this paper cites.
Large language models are zero-shot reasoners
Original
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Original
Long Ouyang, Jeff Wu, Xu Jiang, Daniel Li, Srini Iyer, Eric Huang, Mark Chen, and Q. V. Le · 2022
Earlier work this paper cites.
Self-consistency improves chain of thought reasoning in language models
Original
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Original
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, Quoc V. Le, and Denny Zhou · 2022
Earlier work this paper cites.
Sparks of artificial general intelligence: Early experiments with gpt-4
Original
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, and Yin T. Levy · 2023
Earlier work this paper cites.
Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Hong, Zhen Wang, Daisy Wang, and Zhiting Hu · 2023
Earlier work this paper cites.
Large language models are reasoning teachers
Namgyu Ho, Laura Schmid, and Se-Young Yun · 2023
Earlier work this paper cites.

Similar

Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang · 2023
Cited alongside, same era.
The cot collection: Improving zero-shot and few-shot learning of language models via chain-of-thought fine-tuning
Original
Seungone Kim, Se June Joo, Doyoung Kim, Joel Jang, Seonghyeon Ye, Jamin Shin, and Minjoon Seo · 2023
Cited alongside, same era.
Llm-based agent society investigation: Collaboration and confrontation in avalon gameplay
Original
Yihuai Lan, Zhiqiang Hu, Lei Wang, Yang Wang, Deheng Ye, Peilin Zhao, Ee-Peng Lim, Hui Xiong, and Hao Wang · 2023
Cited alongside, same era.
Theory of mind for multi-agent collaboration via large language models
Huao Li, Yu Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Charles Lewis, and Katia Sycara · 2023
Cited alongside, same era.
Exploring large language models for communication games: An empirical study on werewolf
Original

Then

Reasoning with large language models, a survey
Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, and Thomas Bäck · 2024
Later among the works it cites.
Rethinking the bounds of LLM reasoning: Are multi-agent discussions the key?
Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong, and Yangqiu Song · 2024
Later among the works it cites.
Enhance reasoning for large language models in the game werewolf
Original
Shuang Wu, Liwen Zhu, Tao Yang, Shiwei Xu, Qiang Fu, Yang Wei, and Haobo Fu · 2024
Later among the works it cites.
Llms may not be human-level players, but they can be testers: Measuring game difficulty with llm agents
Original
Chang Xiao and Brenda Z. Yang · 2024
Later among the works it cites.
Deliberate reasoning for llms as structure-aware planning with accurate world model, 2024
Siheng Xiong, Ali Payani, Yuan Yang, and Faramarz Fekri · 2024
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Yuzhuang Xu, Shuo Wang, Peng Li, Fuwen Luo, Xiaolong Wang, Weidong Liu, and Yang Liu · 2023

Cited alongside, same era.

Chessgpt: Bridging policy learning and language modeling

Xidong Feng, Yicheng Luo, Ziyan Wang, Hongrui Tang, Mengyue Yang, Kun Shao, David Mguni, Yali Du, and Jun Wang · 2024

Cited alongside, same era.

Can we further elicit reasoning in llms? critic-guided planning with retrieval-augmentation for solving challenging tasks, 2024

Xingxuan Li, Weiwen Xu, Ruochen Zhao, Fangkai Jiao, Shafiq Joty, and Lidong Bing · 2024

Cited alongside, same era.

Making new connections: Llms as puzzle generators for the new york times’ connections word game

Original

Tim Merino, Sam Earle, Ryan Sudhakaran, Shyam Sudhakaran, and Julian Togelius · 2024

Cited alongside, same era.

Language agents with reinforcement learning for strategic play in the werewolf game

Zelai Xu, Chao Yu, Fei Fang, Yu Wang, and Yi Wu · 2024

Later among the works it cites.

Tree of thoughts: Deliberate problem solving with large language models

Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2024

Later among the works it cites.

Chain of preference optimization: Improving chain-of-thought reasoning in llms

Original

Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, and Min Lin · 2024

Later among the works it cites.