Summary of Llms May Not Be Human-level Players, but They Can Be Testers: Measuring Game Difficulty with Llm Agents, by Chang Xiao et al.
LLMs May Not Be Human-Level Players, But They Can Be Testers: Measuring Game Difficulty with LLM Agents
by Chang Xiao, Brenda Z. Yang
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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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 research explores the potential of Large Language Models (LLMs) in measuring game difficulty. The authors propose a framework using LLM agents to test two popular strategy games: Wordle and Slay the Spire. They find that while LLMs don’t outperform human players, their performance, guided by simple prompting techniques, strongly correlates with human-rated difficulty. This suggests LLMs can aid in game development testing. The authors provide guidelines for incorporating LLMs into this process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses super smart computers called Large Language Models (LLMs) to see if they can help figure out how hard a game is. They tried it on two popular games and found that even though the computer didn’t play as well as humans, it could tell when the game was getting harder or easier. This means these special computers might be helpful in designing new games. |
Keywords
* Artificial intelligence * Prompting
