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Sentimate: Learning to play chess through natural language processing
Original
Isaac Kamlish, Isaac Bentata Chocron, and Nicholas McCarthy. 2019 · 1907
Earlier work this paper cites.
Thinking and reasoning
Alan Garnham and Jane Oakhill. 1994 · 1994
Earlier work this paper cites.
Learning to play the game of chess
Sebastian Thrun. 1994 · 1994
Earlier work this paper cites.
Language models are few-shot learners
Original
Tom B Brown. 2020 · 2005
Earlier work this paper cites.
The chess transformer: Mastering play using generative language models
Original
David Noever, Matt Ciolino, and Josh Kalin. 2020 · 2008
Earlier work this paper cites.
Artificial intelligence: a modern approach
Stuart J Russell and Peter Norvig. 2016 · 2016
Earlier work this paper cites.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
Original
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al. 2017 · 2017
Earlier work this paper cites.
Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
Earlier work this paper cites.
Learning chess with language models and transformers
Original
Michael DeLeo and Erhan Guven. 2022 · 2022
Earlier work this paper cites.

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Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2022 · 2022
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Emergent abilities of large language models
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Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022 · 2022
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Learning to play chess from textbooks (leap): a corpus for evaluating chess moves based on sentiment analysis

Then

Learning the latent rules of a game from data: A chess story
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Ben Fauber. 2024 · 2024
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Language is primarily a tool for communication rather than thought
Evelina Fedorenko, Steven T Piantadosi, and Edward AF Gibson. 2024 · 2024
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Chessgpt: Bridging policy learning and language modeling
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Can large language models play games? a case study of a self-play approach
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Hongyi Guo, Zhihan Liu, Yufeng Zhang, and Zhaoran Wang. 2024 · 2024
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Raising the bar: Investigating the values of large language models via generative evolving testing
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2024

Explore the Reasoning Capability of LLMs in the Chess Testbed

Wang, Shu, Ji, Lei, Wang, Renxi et al.

Understand

Reasoning is a central capability of human intelligence.

In recent years, with the advent of large-scale datasets, pretrained large language models have emerged with new capabilities, including reasoning.
However, these models still struggle with long-term, complex reasoning tasks, such as playing chess.
Based on the observation that expert chess players employ a dual approach combining long-term strategic play with short-term tactical play along with language explanation, we propose improving the reasoning capability of large language models in chess by integrating annotated strategy and tactic.

Built on

Sentimate: Learning to play chess through natural language processing
Original
Isaac Kamlish, Isaac Bentata Chocron, and Nicholas McCarthy. 2019 · 1907
Earlier work this paper cites.
Thinking and reasoning
Alan Garnham and Jane Oakhill. 1994 · 1994
Earlier work this paper cites.
Learning to play the game of chess
Sebastian Thrun. 1994 · 1994
Earlier work this paper cites.
Language models are few-shot learners
Original
Tom B Brown. 2020 · 2005
Earlier work this paper cites.
The chess transformer: Mastering play using generative language models
Original
David Noever, Matt Ciolino, and Josh Kalin. 2020 · 2008
Earlier work this paper cites.
Artificial intelligence: a modern approach
Stuart J Russell and Peter Norvig. 2016 · 2016
Earlier work this paper cites.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
Original
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al. 2017 · 2017
Earlier work this paper cites.
Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
Earlier work this paper cites.
Learning chess with language models and transformers
Original
Michael DeLeo and Erhan Guven. 2022 · 2022
Earlier work this paper cites.

Similar

Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Original
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2022 · 2022
Cited alongside, same era.
Chess as a testbed for language model state tracking
Shubham Toshniwal, Sam Wiseman, Karen Livescu, and Kevin Gimpel. 2022 · 2022
Cited alongside, same era.
Emergent abilities of large language models
Original
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022 · 2022
Cited alongside, same era.
Learning to play chess from textbooks (leap): a corpus for evaluating chess moves based on sentiment analysis

Then

Learning the latent rules of a game from data: A chess story
Original
Ben Fauber. 2024 · 2024
Closest in time.
Language is primarily a tool for communication rather than thought
Evelina Fedorenko, Steven T Piantadosi, and Edward AF Gibson. 2024 · 2024
Closest in time.
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 · 2024
Closest in time.
Can large language models play games? a case study of a self-play approach
Original
Hongyi Guo, Zhihan Liu, Yufeng Zhang, and Zhaoran Wang. 2024 · 2024
Closest in time.
Raising the bar: Investigating the values of large language models via generative evolving testing
Original
Han Jiang, Xiaoyuan Yi, Zhihua Wei, Ziang Xiao, Shu Wang, and Xing Xie. 2024 · 2024

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

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Toolgen: Unified tool retrieval and calling via generation

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Renxi Wang, Xudong Han, Lei Ji, Shu Wang, Timothy Baldwin, and Haonan Li. 2024a

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