TL;DR
This paper introduces Maia4All, a novel framework that efficiently models individual human decision-making in chess with minimal data, significantly reducing data requirements from thousands to just twenty games.
Contribution
Maia4All presents a two-stage optimization approach that leverages prototypes and ability levels to personalize AI behavior modeling with limited data, advancing human-AI alignment in chess.
Findings
Accurately predicts individual chess moves with only 20 games
Reduces data requirement from 5,000 to 20 games
Demonstrates applicability to personalized LLMs
Abstract
As humans seek to collaborate with, learn from, and better understand artificial intelligence systems, developing AIs that can accurately emulate individual decision-making becomes increasingly important. Chess, a long-standing AI benchmark with precise skill measurement, offers an ideal testbed for human-AI alignment. However, existing approaches to modeling human behavior require prohibitively large amounts of data from each individual, making them impractical for new or sparsely represented users. In this work, we introduce Maia4All, a framework designed to learn and adapt to individual decision-making styles efficiently, even with limited data. Maia4All achieves this through a two-stage optimization process: (1) an enrichment step, which bridges population and individual-level human behavior modeling with a prototype-enriched model, and (2) a democratization step, which leverages…
Peer Reviews
Decision·Submitted to ICLR 2025
- I have not seen the idea of matching embeddings of (low-resource) players with other prototypical players, which is quite interesting. - I like the ablation of strength and prototype-initialization without fine-tuning.
- The paper is not sufficiently well-written. There are many things unclear to me as a reader. For example: - How does the model use the population/individual embeddings? - Since you want to predict next moves, why do you need a value head and auxiliary losses? - It is difficult for me to understand what’s going on in Figure 1. - A more fundamental issue with the work is its generality. This paper proposes an initialization technique to solve a problem that is very specific (low-reso
This paper addresses the challenge of behavior modeling in a realistic setting with limited player game records. Existing methods for modeling human behavior require extensive data from each individual, limiting their practical use. The motivation and areas for improvements are clearly outlined.
1. Overall, I find certain aspects of the methodology presentation unclear. The study builds on Maia-2, but several settings from Maia-2 need further explanation. For example, in Section 3.1, population embeddings are introduced, but there is no explanation of how Maia-2's predictions are adjusted solely by varying these embeddings. A brief overview of how Maia-2 works with population embeddings would be helpful. Additionally, in Section 3.2, more detail is needed on the population embeddings an
- The paper introduces a novel application of few-shot learning in chess. The two-stage fine-tuning approach is particularly innovative, allowing the model to leverage rich data from prototype players while efficiently adapting to low-data individuals. - The experimental design is rigorous, with clear evaluation metrics such as move prediction accuracy and perplexity across various data scarcity settings. - Very well written and easy to follow: The paper includes step-by-step explanations and vi
- This paper exclusively studies behavior modeling in the context of chess, raising concerns about whether the methods proposed can generalize effectively to other domains. While the results show that the authors have clearly made an improvement in behavior modeling in chess -- especially when player data is low -- the authors haven't thorough evidence that these methods can improve behavior modeling in other domains which limits the impact of this work. Concretely the authors say "Our work prov
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