Bridging the Gap between Expert and Language Models: Concept-guided Chess Commentary Generation and Evaluation (2025)

Abstract: Deep learning-based expert models have reached superhuman performance in decision-making domains such as chess and Go. However, it is under-explored to explain or comment on given decisions although it is important for model explainability and human education. The outputs of expert models are accurate, but yet difficult to interpret for humans. On the other hand, large language models (LLMs) can produce fluent commentary but are prone to hallucinations due to their limited decision-making capabilities. To bridge this gap between expert models and LLMs, we focus on chess commentary as a representative task of explaining complex decision-making processes through language and address both the generation and evaluation of commentary. We introduce Concept-guided Chess Commentary generation (CCC) for producing commentary and GPT-based Chess Commentary Evaluation (GCC-Eval) for assessing it. CCC integrates the decision-making strengths of expert models with the linguistic fluency of LLMs through prioritized, concept-based explanations. GCC-Eval leverages expert knowledge to evaluate chess commentary based on informativeness and linguistic quality. Experimental results, validated by both human judges and GCC-Eval, demonstrate that CCC generates commentary which is accurate, informative, and fluent.

## introduction artificial intelligence (ai) has achieved superhuman performance in various decision-making tasks, particularly in abstract strategy games like chess and go. milestones such as deep blue's victory over the world chess champion @xcite and alphago's defeat of top human go players highlight ai's capabilities in solving complex problems @xcite . while these expert models deliver highly accurate decisions, they correspondence to: jungseul ok (jungseul@postech.ac.kr) often lack interpretability, which is critical for human education and trust in ai systems. the strategic insights and rationales behind decisions are often explained through natural language commentary @xcite . large language models (llms) exhibit their outstanding performance in generating fluent natural language. however, llms often struggle with hallucinations due to their limited capability in complex decisionmaking and lack of domain-specific knowledge. we aim to bridge the gap between expert and language models. specifically, we focus on the task of chess commentary generation to explain given decisions. although chess is a resourceful testbed with extensive dataset and study @xcite @xcite , the chess commentary generation has two main challenges: (i) producing accurate and insightful commentary, which requires deep chess knowledge and linguistic ability, and (ii) developing evaluation metrics to assess commentary quality, which is overlooked in previous research. although language models can generate fluent natural language, they lack the chess-specific knowledge required for chess commentary generation. even a model @xcite trained on chess-related data struggles in reasoning and understanding complex positions. one promising approach is to integrate expert models with language models. however, prior attempts @xcite directly feeding the decision-making process of expert models to language models are inadequate because the decisionmaking process is hard to interpret for language models. to address them, we introduce an effective approach using concept-based explanations of expert models. by extracting and prioritizing concepts that the expert model focuses on, we guide the language model to concentrate on the most important aspects of the game. this results in commentary that is both linguistically fluent and strategically insightful. figure 1 illustrates previous approaches and our approach. our experiments demonstrate that our approach achieves human-level correctness in commentary generation, while outperforming baselines and human-generated comments in informativeness (relevance, completeness) and linguistic quality (clarity, fluency). evaluating chess commentary generation is another challenge task. previous works @xcite @xcite rely on similarity-based metrics such as bleu, which are insufficient due to the inherently diverse nature of commentary. different commentators may focus on distinct aspects of a position, such as attack strategies or defensive plans. in tasks like summarization or translation, which share the same challenges, llm-based evaluation metrics @xcite are proposed to assess multiple dimensions. we adopt g-eval @xcite by incorporating expert model guidance for chess knowledge. we measure the commentary's informativeness (relevance, completeness) and linguistic quality (clarity, fluency). through our experiments, we show that our proposed method correlates well with human judgments, offering a more reliable metric for commentary evaluation. our contributions are as follows: ## related work chess commentary generation chess commentary generation is generating a comment for a chess move. @xcite first address the task by utilizing web-crawled data to form a chess commentary dataset, framing commentary generation as a sequence prediction problem. building on this, @xcite incorporate domainspecific chess knowledge using internal chess models, improving quality and contextual relevance of generated comments. @xcite , integrate bart @xcite and an external chess engine for more reliable move evaluation. however, their system classifies moves into predefined categories (e.g., excellent, good, inaccuracy, mistake, blunder), without deeper understanding of the model decision-making process. in contrast, we leverage concept-based explanation to extract chess concepts from an expert model to understand the rationale behind the decision. not limited to chess commentary, @xcite fine-tune an llm on chess-related data, to leverage chess skills, not only the linguistic ability. however, we demonstrate that its understanding of chess knowledge is inferior to gpt-4o (openai, 2023) (section 4.4). concept-based explanation in chess concepts are high-level abstractions commonly shared within a community, enabling efficient communication. in chess, concepts such as "king safety" (i.e., all potential threats against the king) condense complex strategies into understandable terms, allowing players to communicate effectively without lengthy explanations. these concepts are understandable to both humans and language models, serving as a bridge between human intuition and neural networks. concept-based explanations aim to make a model interpretable by aligning its internal decision-making process with these shared concepts, assuming that such concepts are linearly embedded in the representation space @xcite @xcite . this assumption is validated in chess domains @xcite for chess expert models like stockfish @xcite , alphazero @xcite , and their open-source versions, such as leelachesszero (authors, 2024). @xcite train a post-hoc concept bottleneck model, and the classifier following the concept bottleneck model is directly interpreted as the global importance of concepts for a class. however, they focus on finding global concept importance per class, without addressing the varying significance of concepts for individual inputs. we address prioritization of concepts for individual inputs, or local importance, to determine the influence of each concept in specific situations. ## method: generation and evaluation we propose two methods to address chess commentary generation (section 3.1) and chess commentary evaluation (section 3.2). ## discussions language model as an explanation form our work shows that the ccc framework effectively transfers ai-driven chess knowledge to human users. beyond concept-based explanation, language models can act as a crucial medium between the expert model's internal reasoning and the enduser. this connection facilitates more intuitive and understandable feedback than traditional explanation methods like saliency-based, which suffer from issues of inconsistency and unreliability. by employing language-based form of explanation, the transparency of the explanation can be improved, making the evaluation of the model's reliability more straightforward. fine-tuning with gcc-eval we validate that gcc-eval is well-correlated with human evaluation. one promising direction to improve the quality of chess commentary is to incorporate gcc-eval as a training objective, replacing human evaluator. by optimizing models to directly align with this evaluative criterion, we can better ensure that the generated commentary meets the standards of human chess experts. this approach offers a potential pathway toward more robust and humanaligned commentary systems in future applications. applicability of ccc while we focus on chess in this paper as a first step, our approach is applicable to other domains, such as science @xcite , medical imaging @xcite , and games @xcite where concept-based explanation methods have been actively studied. well-established concept extractors and relevant datasets in those domains make the domains suitable for applying our method. results with pgn chess notation we also verify that gpt-4o understands pgn notation, not only fen notation. inferring from ccc results with pgn notation, the differences from that with fen notation are not noticible, but we cannot validate it in large scale because our dataset does not contain the game progresses which is required for using pgn notation. appendix f provides the results with pgn notation. ## conclusions in this paper, we propose methods for chess commentary generation (ccc) and evaluation (gcc-eval). ccc integrates expert and language models through concept-based explanations, utilizing techniques such as prioritization, few-shot learning, and chain-of-thought prompting to align effectively with expert knowledge. ccc either surpasses or matches the quality of human-generated commentary, demonstrating the capability of llms to express expert-level understanding and potentially enhance learning for human users. we also present gcc-eval, a multi-dimensional evaluation framework that incorporates chess-specific knowledge to assess chess commentary. the strong correlation between human evaluation and gcc-eval validates the robustness. these findings underscore promising future research directions, including using a language model as an explanation method and using gcc-eval fine-tuning chess commentary generation models.