Papers with Go
Understanding Game-Playing Agents with Natural Language Annotations (2022.acl-short)
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| Challenge: | a new dataset contains 10K human-annotated Go games. |
| Approach: | They propose to use natural language annotations to predict game terms from Go models . they use linear probing to predict domain-specific terms from the model's intermediate state representations . |
| Outcome: | The proposed model can predict domain-specific terms from human-annotated Go games . the proposed model is based on a dataset containing 10K human-rannotated games based in the game . |
Humans Keep It One Hundred: an Overview of AI Journey (2020.lrec-1)
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Tatiana Shavrina, Anton Emelyanov, Alena Fenogenova, Vadim Fomin, Vladislav Mikhailov, Andrey Evlampiev, Valentin Malykh, Vladimir Larin, Alex Natekin, Aleksandr Vatulin, Peter Romov, Daniil Anastasiev, Nikolai Zinov, Andrey Chertok
| Challenge: | Artificial General Intelligence (AGI) is showing growing performance in numerous applications - beating human performance in Chess and Go, using knowledge bases and text sources to answer questions and even pass human examination. |
| Approach: | They propose to use knowledge bases and text sources to answer questions to improve AI performance on knowledge bases, reasoning and text generation. |
| Outcome: | The proposed AI Journey system passed the final native language exam in Russian with a high score of 69%, with 68% being an average human result. |
Fine-grained Typing of Emerging Entities in Microblogs (2021.findings-emnlp)
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| Challenge: | Graus et al., 2018) defined emerging entities as those that appear in contexts that emphasize their novelty, and attempted to discover emerging entities from microblogs. |
| Approach: | They propose a task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. |
| Outcome: | The proposed model can type 'homographic' emerging entities without relying on prior knowledge of the target entity. |
MASTER: A Multi-Agent System with LLM Specialized MCTS (2025.naacl-long)
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Bingzheng Gan, Yufan Zhao, Tianyi Zhang, Jing Huang, Li Yusu, Shu Xian Teo, Changwang Zhang, Wei Shi
| Challenge: | Large Language Models (LLMs) are increasingly being explored for problem-solving tasks . their strategic planning capability is often viewed with skepticism due to their limited planning capabilities. |
| Approach: | They propose a framework that coordinates agent recruitment and communication through LLM specialized MCTS. |
| Outcome: | The proposed framework achieves 76% accuracy on HotpotQA and 80% on WebShop . it relies on extensive sampling simulations to approximate the true reward distribution . |
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. |
Towards Quantifying Commonsense Reasoning with Mechanistic Insights (2025.naacl-long)
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| Challenge: | Recent studies have evaluated commonsense reasoning abilities using text-based tasks. |
| Approach: | They propose to capture commonsense knowledge in a graphical representation of 37 daily human activities in graphical form and frame them to frame commonsensical queries. |
| Outcome: | The proposed model can frame an enormous number of commonsense queries ( 10 17) and perform rigorous evaluations of common sense reasoning in LLMs. |
VulLibGen: Generating Names of Vulnerability-Affected Packages via a Large Language Model (2024.acl-long)
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Tianyu Chen, Lin Li, ZhuLiuchuan ZhuLiuchuan, Zongyang Li, Xueqing Liu, Guangtai Liang, Qianxiang Wang, Tao Xie
| Challenge: | Existing work on affected package identification is limited by large language models . a recent study shows that 84% third-party packages contain security vulnerabilities . |
| Approach: | They propose a method to use LLM to generate the affected package . they propose supervised fine-tuning, retrieval augmented generation and a local search algorithm . |
| Outcome: | The proposed method has an average precision of 0.806 for identifying vulnerable packages in four most popular ecosystems in GitHub Advisory. |
ODASim: Ordered, Distinctive and Absolute Semantic Similarity for Code Explanation Evaluation (2026.findings-acl)
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Prince Kumar, Vitobha Munigala, Jaydeep Sen, Ashish Mittal, Vishwajeet Kumar, Srikanth G. Tamilselvam
| Challenge: | Existing methods for code explanations fail to distinguish correct from partially or fully incorrect explanations and their similarity scores are poorly calibrated. |
| Approach: | They propose a model-agnostic graded fine-tuning framework that learns calibrated similarity representations between code and explanations to support fine-grained supervision and evaluation. |
| Outcome: | The proposed framework improves F1 score and ECE scores on two embedding models and reduces expected calibration error. |
LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging (2026.acl-long)
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| Challenge: | Low-Rank Adaptation (LoRA) is a parameter-efficient approach for fine-tuning large language models. |
| Approach: | They propose a low-rank Adaptation framework that automatically selects and merges LoRA adapters at the instance level without additional training. |
| Outcome: | The proposed framework outperforms training-based baselines on some tasks upto a margin of 3.6% while remaining competitive on other tasks and maintaining inference throughput. |