Papers with chess
Complete Chess Games Enable LLM Become A Chess Master (2025.naacl-short)
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| Challenge: | Large language models (LLMs) have shown remarkable abilities in text generation, question answering, language translation, reasoning and many other tasks. |
| Approach: | They propose a Large language model that can play chess games by transforming a game into a textual format with the best move represented in the Forsyth-Edwards Notation. |
| Outcome: | The proposed model achieves professional-level Elo rating of 1788 in matches against the standard Elo-rated Stockfish when permitted to sample 10 times. |
Explore the Reasoning Capability of LLMs in the Chess Testbed (2025.naacl-short)
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| Challenge: | a recent study shows that large language models struggle with long-term, complex reasoning tasks. |
| Approach: | They propose to integrate annotated strategy and tactic into large language models to improve reasoning capability. |
| Outcome: | The proposed model performs better than GPT, Claude, and Gemini models . it integrates annotated strategy and tactic into the model . |
Cards Against Contamination: TCG-Bench for Difficulty-Scalable Multilingual LLM Reasoning (2026.findings-eacl)
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| Challenge: | Recent studies find 25-50% of evaluation datasets appear in training corpora . contamination hinders the possibility to differentiate memorization and reasoning skills. |
| Approach: | They propose a two-player trading card game that is contaminated by a public engine and hidden card implementations to prevent benchmark saturation. |
| Outcome: | The proposed benchmark is based on a new two-player trading card game similar to Magic: The Gathering. |
Bridging the Gap between Expert and Language Models: Concept-guided Chess Commentary Generation and Evaluation (2025.naacl-long)
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| Challenge: | Experimental results show that expert models generate accurate, informative and fluent commentary, but are prone to hallucinations due to their limited decision-making capabilities. |
| Approach: | They propose a concept-guided chess commentary generation and a GPT-based Chess Commentary Evaluation to bridge this gap between expert models and large language models. |
| Outcome: | The proposed model is accurate, informative, and fluent. |
Using tournaments to calculate AUROC for zero-shot classification with LLMs (2025.findings-emnlp)
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| Challenge: | Large language models perform well on zero-shot classification tasks, but are difficult to compare to supervised classifiers due to the lack of a modifiable decision boundary. |
| Approach: | They propose a method that transforms binary classification tasks into pairwise comparisons between instances within a dataset. |
| Outcome: | The proposed method produces relative rankings of instances using the Elo rating system . |