用分层专家提示让大模型首次击败星际2顶级AI。
Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First Time
- 设计分层专家提示框架，分级处理任务重要性
- 首次在文本版星际2中战胜精英级内置AI
- 适合对复杂决策和大模型应用感兴趣的读者
自大型语言模型（LLM）出现以来，其已广泛应用于写作、翻译和搜索等领域。然而，在星露谷2环境中的复杂决策任务方面仍具巨大潜力。为解决相关知识不足及对不同重要性子任务控制不佳的问题，本文提出分层专家提示（HEP）方法。该方法通过专家级战术知识提升对游戏局势的理解，并利用分层框架改善不同重要性任务的处理质量。实验结果表明，该方法首次在文本版星际2中击败最高难度（精英级）内置智能体，并在其他难度上持续优于基线方法。研究验证了该方法在应对复杂决策挑战中的实用性。视频回放可观看于 https://www.bilibili.com/video/BV1uz42187EF 及 https://youtu.be/dO3PshWLV5M，代码已开源至 https://github.com/luchang1113/HEP-LLM-play-StarCraftII。
原文摘要 · Abstract (English)
Since the emergence of the Large Language Model (LLM), LLM has been widely used in fields such as writing, translating, and searching. However, there is still great potential for LLM-based methods in handling complex tasks such as decision-making in the StarCraft II environment. To address problems such as lack of relevant knowledge and poor control over subtasks of varying importance, we propose a Hierarchical Expert Prompt (HEP) for LLM. Our method improves the understanding of game situations through expert-level tactical knowledge, improving the processing quality of tasks of varying importance through a hierarchical framework. Our approach defeated the highest level (Elite) standard built-in agent in TextStarCraft II for the first time and consistently outperformed the baseline method in other difficulties. Our experiments suggest that the proposed method is a practical solution for tackling complex decision-making challenges. The replay video can be viewed on https://www.bilibili.com/video/BV1uz42187EF and https://youtu.be/dO3PshWLV5M, and our codes have been open-sourced on https://github.com/luchang1113/HEP-LLM-play-StarCraftII.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。