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LLM GameLab: An Interactive Platform for Testing Large Language Models in Board Games

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

While large language models are constantly evaluated in various skills, such as math, general knowledge, and coding, their ability to understand and follow game rules has not yet been deeply explored. The latter is especially important as it allows testing whether LLMs can operate within predefined limits without deviating or making illogical mistakes. Therefore, this demo paper presents a tool for interacting with LLMs in board games. The tool allows the creation of players with different large language models pitted against each other or to play in human vs. LLM mode. The platform includes rules predefined in prompts for four simple games based on Tic-Tac-Toe and Connect Four. Each player can be evaluated to account for their illegal movements, wins, draws, losses, and response times. The application also allows for the creation of new games, opening up the possibility of examining LLM behavior in situations they have not previously encountered.

Idioma originalInglés
Título de la publicación alojadaMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Proceedings
EditoresInês Dutra, Alípio M. Jorge, Carlos Soares, João Gama, Mykola Pechenizkiy, Paulo Cortez, Sepideh Pashami, Arian Pasquali, Nuno Moniz, Pedro H. Abreu
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas486-490
Número de páginas5
ISBN (versión impresa)9783032061287
DOI
EstadoPublicada - 2026
EventoEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025 - Porto, Portugal
Duración: 15 sep. 202519 sep. 2025

Serie de la publicación

NombreLecture Notes in Computer Science
Volumen16022
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

ConferenciaEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025
País/TerritorioPortugal
CiudadPorto
Período15/09/2519/09/25

Nota bibliográfica

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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