China

Plain Language Summaries (PLS) play a critical role in improving health literacy, enabling informed decision-making and equitable healthcare access. However, writing PLS requires domain expertise and is time-consuming, making automation a valuable strategy for improving accessibility at scale. Automated methods often prioritize efficiency over comprehension, and the unique simplification requirements of medical documents challenge generic solutions. We present a multi-agent system for generating PLS, using Cochrane PLS as a proof of concept. The system decomposes simplification in four tasks, each handled by specialized agents: information extraction, writing, diagnostic, and evaluation. It integrates a medical glossary (20,637 terms) and a statistical analyzer that evaluates text patterns to guide revisions. We evaluated on 100 Cochrane abstracts using three models: Gemini-2.5-Pro, GPT-5 and the open model GPT-OSS-120B. The system achieved superior performance across semantic similarity, factual alignment, and readability metrics compared to single-prompt baselines. By combining AI agents with specific evaluation tools, this work offers a scalable solution that reduces the health literacy gap by making medical information more understandable to the public through accurate, readable summaries.

EMNLP 2025

A Multi-Agent Framework with Diagnostic Feedback for Iterative Plain Language Summary Generation from Cochrane Medical Abstracts

medical writing

multi agent

text simplification

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*





<br>

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

workshop paper

Individuals with intellectual disabilities often have difficulties in comprehending complex texts. While many text-to-image models prioritize photorealism over cognitive accessibility, it is not clear how visual illustrations relate to text simplifications (TS) generated from them. This paper presents a structured vision language model (VLM) prompting framework for generating cognitively accessible images from simplified texts. We designed five prompt templates, i.e., Basic Object Focus, Contextual Scene, Educational Layout, Multi-Level Detail, and Grid Layout, each following distinct spatial arrangements while adhering to accessibility constraints such as object count limits, spatial separation, and content restrictions. Using 400 sentence-level TS pairs from four established text simplification datasets (OneStopEnglish, SimPA, Wikipedia, ASSET), we conducted a two-phase evaluation: Phase 1 assessed template effectiveness with CLIP similarity scores, and Phase 2 involved expert annotation of generated images across ten visual styles by four accessibility specialists. Results show that the Basic Object Focus template achieved the highest semantic alignment, indicating that visual minimalism enhances accessibility. Expert evaluation further identified Retro style as the most accessible and Wikipedia as the most effective text source. Inter-annotator agreement varied across dimensions, with Text Simplicity showing strong reliability and Image Quality proving more subjective. Overall, our framework offers practical guidelines for accessible content creation and underscores the importance of structured prompting in AI-generated visual accessibility tools.

Template-Based Text-to-Image Alignment for Language Accessibility: A Study on Visualizing Text Simplifications

Automatic readability assessment plays a key role in ensuring effective communication between humans and language models. Despite significant progress, the field is hindered by inconsistent definitions of readability and measurements that rely on surface-level text properties. In this work, we investigate the factors shaping human perceptions of readability through the analysis of 1.2k judgments, finding that, beyond surface-level cues, information content and topic strongly shape text comprehensibility. Furthermore, we evaluate 15 popular readability metrics across 5 datasets, contrasting them with 5 more nuanced, model-based metrics. Our results show that four model-based metrics consistently place among the top 4 in rank correlations with human judgments, while the best performing traditional metric achieves an average rank of 7.8. These findings highlight a mismatch between current readability metrics and human perceptions, pointing to model-based approaches as a more promising direction.

Readability Reconsidered: A Cross-Dataset Analysis of Reference-Free Metrics

This work presents a system for on-device text simplification that enables users to process sensitive text without relying on cloud-based services. Through the use of quantization techniques and a novel approach to controllable text simplification, we reduce model size by up to 75% with minimal performance degradation. Our models demonstrate efficient state-of-the-art results using a synthetic dataset of 2,909 examples, outperforming prior work trained on 300K examples. This efficiency stems from: (1) a single control token strategy that precisely targets specific reading levels, (2) a contrastive training approach that enriches model understanding through exposure to multiple simplification levels, and (3) individual models that dedicate full parameter capacity to specific reading level transformations. Our best models achieve up to 82.18 BLEU (at the Advanced level) and 46.12 SARI (at the Elementary level) on standard benchmarks, with performance preserved even after aggressive quantization. This work is implemented as a collaboration with the Mozilla AI team to processes text entirely locally, ensuring sensitive information never leaves the user's device. We have a demonstration video (https://youtu.be/TzmaxnARMzg) and a web demo available at: (https://pablorom2004.github.io/Simplification-Web-Demo)

Efficient On-Device Text Simplification for Firefox with Synthetic Data Fine-Tuning

In this contribution to the CEFR level simplification TSAR 2025 Shared Task, we propose two systems, EZ-SCALAR and SAGA, that implement two differing approaches to prompting LLMs for proficiency-adapted simplification. Our results place us in the middle of the participating teams, and reveal that using external lexical resources to guide simplification improves overall results.

GRIPF at TSAR 2025 Shared Task: Towards controlled CEFR level simplification with the help of inter-model interactions

Automatic Text Simplification (TS) makes complex texts more accessible but often lacks control over target readability levels. We propose a lightweight, prompt-based approach to English TS that explicitly aligns outputs with CEFR proficiency standards. Our method employs a three-stage pipeline, guided by rule-informed prompts inspired by expert strategies. In the TSAR 2025 Shared Task, our system achieved competitive performance, with stronger results at B1 level and challenges at A2 level due to over-simplification. These findings highlight the promise of prompt-based CEFR-oriented simplification and the need for more flexible constraint design.

ITU NLP at TSAR 2025 Shared Task: A Three-Stage Prompting Approach for CEFR-Oriented Text Simplification

taskGen ranked as 6th best team in the TSAR shared task for English text adaptation to a target CEFR level. Our approaches consisted of prompting a Llama-3.1-8B-Instruct model with linguistic descriptors of the target level, examples of adaptations and multi-step approaches. Our best approach, 13th in the overall ranking, applied an ensemble approach with a voting mechanism to find the most adequate among 11 texts, each produced by a different prompt to the same model.

taskGen at TSAR 2025 Shared Task: Exploring prompt strategies with linguistic knowledge

This paper presents an approach to automated text simplification for CEFR A2 and B1 levels using large language models and prompt engineering.
We evaluate seven models across three prompting strategies: short, descriptive, and descriptive with examples. A two-round evaluation system using LLM-as-a-Judge and traditional metrics for text simplification determines optimal model-prompt combinations for final submissions. Results demonstrate that descriptive prompts consistently outperform other strategies across all models, achieving 46-65\% of first-place rankings. Qwen3 shows superior performance for A2-level simplification, while B1-level results are more balanced across models. The LLM-as-a-Judge evaluation method shows strong alignment with traditional metrics while providing enhanced explainability.

EasyJon at TSAR 2025 Shared Task: Evaluation of Automated Text Simplification with LLM-as-a-Judge

We present an agent-based system for the TSAR 2025 Shared Task on Readability-Controlled Text Simplification, which requires simplifying English paragraphs from B2+ levels to target A2 or B1 levels while preserving meaning. Our approach employs specialized agents for keyword extraction, text generation, and evaluation, coordinated through an iterative refinement loop. The system integrates a CEFR vocabulary classifier, pretrained evaluation models, and few-shot learning from trial data. Through iterative feedback between the evaluator and writer agents, our system automatically refines outputs until they meet both readability and semantic preservation constraints. This architecture achieved Xth position among participating teams, showing the effectiveness of combining specialized LLMs with automated quality control strategies for text simplification.


Uniandes at TSAR 2025 Shared Task: Multi-Agent CEFR Text Simplification with Automated Quality Assessment and Iterative Refinement

This paper describes our submissions to the TSAR 2025 Shared Task on Readability-Controlled Text Simplification. We present a comparative study of three architectures: a minimal rule-based baseline, an expert-enhanced system, and a multi-stage generative pipeline using a T5 model in a zero-shot setting. Because per-instance official scores were not available at the time of analysis, we perform a principled sensitivity analysis via simulated paired bootstrap to assess robustness of our comparative claims. Under a wide range of reasonable assumptions the simpler, more constrained systems show substantially better automatic scores for semantic fidelity and the composite AUTORANK metric. We include diagnostic failure analysis grounded in actual system outputs, discuss limitations of embedding-based guardrails, and provide concise reproducibility notes in the Appendix. Full code, experimental configurations, and outputs will be released upon acceptance to ensure complete reproducibility.


HOPE at TSAR 2025 Shared Task: Balancing Control and Complexity in Readability-Controlled Text Simplification

This paper describes the system submission of our team OUNLP to the TSAR-2025 shared task on readability-controlled text simplification. Based on the analysis on \Naive Prompting-based method on text simplification, we discovered an interesting finding that the performance of text simplification is highly related the gap between source CERF~\cite{arase2022cefr} level and target CERF level. Inspired by this finding, we propose to two multi-round simplification methods: rule-based simplification (MRS-Rule) and jointly rule-based LLM simplification (MRS-Joint), which are all generated with AI. Our system ranked 7 over 20 teams. Later improvements demonstrates that AI generated code with robust evaluation metrics for verification are promising methods to produce reliable, readability-controlled text simplifications~\footnote{\url{https://github.com/Rickie2k6/Sentence_Simplification}}.

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