Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/135805| Title: | Consistent game content creation via function calling for large language models |
| Authors: | Gallotta, Roberto Liapis, Antonios Yannakakis, Georgios N. |
| Keywords: | Video games -- Design Level design (Computer science) Computational intelligence Artificial intelligence |
| Issue Date: | 2024 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Citation: | Gallotta, R., Liapis, A., & Yannakakis, G. (2024, August). Consistent game content creation via function calling for large language models. In 2024 IEEE Conference on Games (CoG) (pp. 1-4). IEEE. |
| Abstract: | Tools for designing content require a medium that allows the designer to efficiently express their creativity, and a system that ensures the content being designed adheres to the domain of interest. Interacting with Large Language Models (LLMs) via natural language is extremely intuitive for a human designer, although it remains largely unexplored. However, this approach has a limitation: LLMs are prone to hallucinations and they tend to ignore parts of the user request in their responses. One workaround is to let LLM use tools such as function calling to ensure consistency of the content. We formalise this approach by proposing LLMaker, a general framework for consistent video game content generation empowered by LLMs, bridging the gap between creative vision and technical execution. We demonstrate LLMaker’s application in generating dungeon crawler level layouts, comparing it against alternative LLM-based methods for content generation over multiple tests, testing for consistency of the outputs and elapsed time per request. |
| URI: | https://www.um.edu.mt/library/oar/handle/123456789/135805 |
| Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| consistent_game_content_creation_via_function_calling_for_large_language_models.pdf Restricted Access | 201.44 kB | Adobe PDF | View/Open Request a copy |
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