Summary of Grammar-based Game Description Generation Using Large Language Models, by Tsunehiko Tanaka et al.
Grammar-based Game Description Generation using Large Language Models
by Tsunehiko Tanaka, Edgar Simo-Serra
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework for generating Game Description Language (GDL) descriptions from natural language using Large Language Models (LLMs). The approach consists of two stages: first, a minimal grammar is generated based on GDL specifications; second, the game description is iteratively improved through grammar-guided generation. A specialized parser identifies valid subsequences and candidate symbols from LLM responses to ensure grammatical correctness. Experimental results show that this iterative improvement approach outperforms baseline methods in generating grammatically accurate game descriptions. The framework employs LLMs for natural language processing, GDL specifications for grammar definition, and a parsing mechanism for refining the output. This work has implications for automated game simulation, evaluation, and potential applications in areas such as game development, testing, and analytics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a way to describe games using computers. It’s hard to make this happen because there are many different kinds of games, but the researchers found a solution. They used special computer models that can understand language to help create the game descriptions. The approach has two parts: first, they create a basic structure for the description; then, they refine it to make sure it’s correct. This way of doing things is better than previous methods because it makes the descriptions more accurate. The researchers hope this work will be useful in areas like making games, testing them, and analyzing how people play. |
Keywords
» Artificial intelligence » Natural language processing » Parsing
