用多智能体协作让大模型更准地简化复杂句子
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences
- 设计多智能体协同提示框架，分步处理复杂句
- 在游戏设计语料上简化成功率达70%，优于单智能体的48%
- 适合需要精准语言转换的教育、写作辅助场景
本文针对将复杂句子转化为逻辑清晰的简化句这一挑战，提出一种结合高级提示与多智能体架构的混合方法，以保持语义和逻辑完整性。实验表明，该方法在视频游戏设计文本上成功简化了70%的复杂句子，而单智能体方法仅达到48%的成功率。
原文摘要 · Abstract (English)
This paper addresses the challenge of transforming complex sentences into sequences of logical, simplified sentences while preserving semantic and logical integrity with the help of Large Language Models. We propose a hybrid approach that combines advanced prompting with multi-agent architectures to enhance the sentence simplification process. Experimental results show that our approach was able to successfully simplify 70% of the complex sentences written for video game design application. In comparison, a single-agent approach attained a 48% success rate on the same task.
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