Summary of Large Language Models and Games: a Survey and Roadmap, by Roberto Gallotta et al.
Large Language Models and Games: A Survey and Roadmap
by Roberto Gallotta, Graham Todd, Marvin Zammit, Sam Earle, Antonios Liapis, Julian Togelius, Georgios N. Yannakakis
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 study surveys the current state-of-the-art applications of large language models (LLMs) in and for games. It identifies the various roles LLMs can play within a game, including underexplored areas and promising directions for future uses. The paper reconciles the potential and limitations of LLMs within the games domain, serving as a comprehensive survey and roadmap at the intersection of LLMs and games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) have seen a huge increase in research and public interest recently. This study looks at how LLMs are being used in video games. It shows what different roles LLMs can take on within a game, like making game characters talk or helping players make decisions. The paper also talks about areas that need more work and ideas for the future. |
