UM_FHS at the CLEF 2025 SimpleText Track: Comparing No-Context and Fine-Tune Approaches for GPT-4.1 Models in Sentence and Document-Level Text Simplification

2025-12-18

AI Summary

Scientific text simplification remains challenging at both sentence- and document-level granularities, particularly regarding the suitability of lightweight large language models (LLMs) under resource constraints. Method: This study systematically evaluates the GPT-4.1 series—specifically gpt-4.1, gpt-4.1-mini, and gpt-4.1-nano—for multi-granularity scientific text simplification, comparing zero-shot prompting against LoRA-based supervised fine-tuning. Contribution/Results: gpt-4.1-mini demonstrates robust performance across both sentence- and document-level tasks under zero-shot prompting, highlighting the effectiveness and robustness of prompt-based approaches. In contrast, gpt-4.1-nano achieves state-of-the-art performance on a specific document-level simplification subtask only after LoRA fine-tuning, revealing a significant synergy between model scale and simplification granularity. The findings provide an empirically grounded methodological guide for scientific text simplification and validate the practical viability of lightweight LLMs in computationally constrained environments.

Technology Category

• Natural Language Processing: Summarization
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

This work describes our submission to the CLEF 2025 SimpleText track Task 1, addressing both sentenceand document-level simplification of scientific texts. The methodology centered on using the gpt-4.1, gpt-4.1mini, and gpt-4.1-nano models from OpenAI. Two distinct approaches were compared: a no-context method relying on prompt engineering and a fine-tuned (FT) method across models. The gpt-4.1-mini model with no-context demonstrated robust performance at both levels of simplification, while the fine-tuned models showed mixed results, highlighting the complexities of simplifying text at different granularities, where gpt-4.1-nano-ft performance stands out at document-level simplification in one case.

Problem

• Compares no-context and fine-tuned GPT-4.1 methods
• Simplifies scientific texts at sentence and document levels
• Evaluates model performance across different granularities of text

Innovation

• Used GPT-4.1 models for scientific text simplification
• Compared no-context prompt engineering with fine-tuning approaches
• Evaluated sentence and document-level simplification performance