| Challenge: | Recent advances in Large Language Models (LLMs) have enhanced the possibilities for game prototyping, but they face significant challenges. |
| Approach: | They propose a graph-based indexing method for generating novel game variations and an LLM-driven system for consistent game code generation validated by gameplay records. |
| Outcome: | The proposed framework accelerates card game prototyping, reduces human labor, and lowers barriers to entry for game developers. |
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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou
| Challenge: | Existing code-related benchmarks focus on single modality rather than visual game development. |
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LLMsPark: A Benchmark for Evaluating Large Language Models in Strategic Gaming Contexts (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) are increasingly important for their intelligence evaluation. |
| Approach: | They propose a game theory-based evaluation platform that measures LLMs’ decision-making strategies and social behaviors in classic game-theoretic settings. |
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Towards A “Novel” Benchmark: Evaluating Literary Fiction with Large Language Models (2025.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) context windows have enabled them to process inputs over 100K tokens and generate outputs of up to 10K token. |
| Approach: | They propose a multi-level evaluation framework that incorporates ten metrics across the Macro, Meso, and Micro levels and an annotated fiction dataset. |
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On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
| Approach: | They propose a generic workflow for LLM-driven synthetic data generation. |
| Outcome: | The proposed workflows highlight gaps in existing research and outline avenues for future studies. |
GAMEBoT: Transparent Assessment of LLM Reasoning in Games (2025.acl-long)
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| Challenge: | Existing efforts to create benchmarks that move beyond superficial pattern recognition to delve into the profound reasoning skills required for problemsolving face challenges such as insufficient interpretability, performance saturation or data contamination. |
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Game Development as Human-LLM Interaction (2025.acl-long)
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| Challenge: | Currently, game development is a highly specialized task that relies on a complex game engine powered by complex programming languages, preventing many gaming enthusiasts from handling it. |
| Approach: | They propose a chat game engine powered by LLM that allows everyone to develop a custom game using natural language through Human-LLM interaction. |
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A Framework for Exploring Player Perceptions of LLM-Generated Dialogue in Commercial Video Games (2023.findings-emnlp)
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| Challenge: | evaluating the player experience in a roleplaying game augmented with LLM-generated dialogue remains a major challenge. |
| Approach: | They propose a dynamic evaluation framework for the dialogue management systems that govern the task-oriented dialogue often found in roleplaying video games. |
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Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language. |
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ZeroSumEval: An Extensible Framework For Scaling LLM Evaluation with Inter-Model Competition (2025.acl-demo)
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| Challenge: | Recent studies highlight the effectiveness of game-based evaluations for Large Language Models. |
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Learning to Play Like Humans: A Framework for LLM Adaptation in Interactive Fiction Games (2025.findings-acl)
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| Challenge: | Existing approaches prioritize task-specific performance metrics over human-like comprehension of narrative context and gameplay logic. |
| Approach: | They propose a framework that guides Large Language Models to learn and play IF games systematically. |
| Outcome: | The proposed framework aligns LLMs-based agents’ behavior with narrative intent and commonsense constraints to deliver more interpretable, human-like performance. |