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Society of Medical Simplifiers

Chen Lyu, Gabriele Pergola

TL;DR

Medical text simplification aims to make biomedical literature accessible to diverse audiences. The authors introduce the Society of Medical Simplifiers, a five-agent SOM inspired framework that delegates Layperson, Simplifier, Medical Expert, Language Clarifier, and Redundancy Checker roles and uses interaction loops guided by an Agent Selector to iteratively refine medical text. Experiments on the Cochrane Medical Text Simplification Dataset show improvements in readability metrics while maintaining content, signaling the promise of multi-agent LLM architectures for controlled simplification. The work highlights practical implications for making medical knowledge more accessible and outlines avenues for broader model evaluation and framework enhancements.

Abstract

Medical text simplification is crucial for making complex biomedical literature more accessible to non-experts. Traditional methods struggle with the specialized terms and jargon of medical texts, lacking the flexibility to adapt the simplification process dynamically. In contrast, recent advancements in large language models (LLMs) present unique opportunities by offering enhanced control over text simplification through iterative refinement and collaboration between specialized agents. In this work, we introduce the Society of Medical Simplifiers, a novel LLM-based framework inspired by the "Society of Mind" (SOM) philosophy. Our approach leverages the strengths of LLMs by assigning five distinct roles, i.e., Layperson, Simplifier, Medical Expert, Language Clarifier, and Redundancy Checker, organized into interaction loops. This structure allows the agents to progressively improve text simplification while maintaining the complexity and accuracy of the original content. Evaluations on the Cochrane text simplification dataset demonstrate that our framework is on par with or outperforms state-of-the-art methods, achieving superior readability and content preservation through controlled simplification processes.

Society of Medical Simplifiers

TL;DR

Medical text simplification aims to make biomedical literature accessible to diverse audiences. The authors introduce the Society of Medical Simplifiers, a five-agent SOM inspired framework that delegates Layperson, Simplifier, Medical Expert, Language Clarifier, and Redundancy Checker roles and uses interaction loops guided by an Agent Selector to iteratively refine medical text. Experiments on the Cochrane Medical Text Simplification Dataset show improvements in readability metrics while maintaining content, signaling the promise of multi-agent LLM architectures for controlled simplification. The work highlights practical implications for making medical knowledge more accessible and outlines avenues for broader model evaluation and framework enhancements.

Abstract

Medical text simplification is crucial for making complex biomedical literature more accessible to non-experts. Traditional methods struggle with the specialized terms and jargon of medical texts, lacking the flexibility to adapt the simplification process dynamically. In contrast, recent advancements in large language models (LLMs) present unique opportunities by offering enhanced control over text simplification through iterative refinement and collaboration between specialized agents. In this work, we introduce the Society of Medical Simplifiers, a novel LLM-based framework inspired by the "Society of Mind" (SOM) philosophy. Our approach leverages the strengths of LLMs by assigning five distinct roles, i.e., Layperson, Simplifier, Medical Expert, Language Clarifier, and Redundancy Checker, organized into interaction loops. This structure allows the agents to progressively improve text simplification while maintaining the complexity and accuracy of the original content. Evaluations on the Cochrane text simplification dataset demonstrate that our framework is on par with or outperforms state-of-the-art methods, achieving superior readability and content preservation through controlled simplification processes.

Paper Structure

This paper contains 16 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Agent Roles
  4. Interaction Loops
  5. Pipeline of the Framework
  6. Experiment
  7. Preliminary Analyses on the Fixed Number of Iterations
  8. Result and Discussion
  9. Conclusion
  10. Limitations
  11. Agent Role Prompts
  12. Layperson
  13. Medical Expert
  14. Simplifier
  15. Language Clarifier
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Society of Medical Simplifiers framework. Details of the interaction loops are presented at the bottom.
  • Figure 2: Relationship between performance on metrics and the number of evaluation iterations.