E xpert E ase: A Multi-Agent Framework for Grade-Specific Document Simplification with Large Language Models (2024)

Abstract: Text simplification is crucial for making texts more accessible, yet current research primarily focuses on sentence-level simplification, neglecting document-level simplification and the different reading levels of target audiences. To bridge these gaps, we introduce ExpertEase, a multi-agent framework for grade-specific document simplification using Large Language Models (LLMs). ExpertEase simulates real-world text simplification by introducing expert, teacher, and student agents that cooperate on the task and rely on external tools for calibration. Experiments demonstrate that this multi-agent approach significantly enhances LLMs’ ability to simplify reading materials for diverse audiences. Furthermore, we evaluate the performance of LLMs varying in size and type, and compare LLM-generated texts with human-authored ones, highlighting their potential in educational resource development and guiding future research.

## expertease: grade-specific document simplification framework to effectively leverage llms for document-level simplification in educational settings, we propose expertease, a framework that employs multi-agent collaboration to achieve precise, efficient, expertlike simplification. figure 1 illustrates the multiagent setting and the simplification pipeline of ex-pertease, which completes the task through three stages: (1) simplification based on expert knowledge: the expert agent performs the initial simplification by leveraging linguistic knowledge and referring to example materials. (2) tool-assisted calibration: llms receive feedback from readability tools and adjust the text. (3) revision via user feedback: teacher and student agents provide feedback from different perspectives, enabling the expert to further refine the texts. this cooperative process among the agents and tools streamlines the simplification workflow. ## discussion experiments demonstrate that expertease, leveraging multi-agent collaboration, achieves progressively better results across three stages (see appendix a.11 for stage-wise rewriting examples). in this section, we aim to further investigate the consistency and divergence between llm-generated and human-authored rewrites. to this end, we discuss understanding and following the instructions for each role. our findings from three perspectives: readability, linguistic features, and meaning preservation. ## conclusion this paper proposed expertease, an effective framework for grade-specific document simplification. by integrating expert knowledge, feedback mechanisms from external tools, and collaborative inputs from teacher and student agents, our approach significantly enhances llms' ability to generate texts tailored to specified readability levels. furthermore, our findings highlight the importance of agent-level and model-level collaborations in achieving superior performance. moreover, we identified distinct simplification strategies employed by models and humans, suggesting the potential for incorporating human-in-theloop simplification pipelines when producing educational resources. it is worth noting that our framework is universally applicable to various types of llms. not only can it help achieve efficient grade-specific simplification capability, but this multi-agent framework, which incorporates experts, teachers, students, and tools, can also serve as a reference for the development of more educational applications.