A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences
This work addresses sentence simplification for applications like video game design, but it appears incremental as it builds on existing prompting and multi-agent techniques.
The paper tackled the problem of simplifying complex sentences into logical, simplified sequences while preserving semantics, using a hybrid multi-agent prompting approach with Large Language Models, achieving a 70% success rate compared to 48% for a single-agent method.
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.