Explain Less, Understand More: Jargon Detection via Personalized Parameter-Efficient Fine-tuning

TL;DR

This paper introduces efficient, low-resource personalized jargon detection methods using open-source models, significantly improving performance with minimal annotated data and enabling scalable, user-adaptive NLP systems.

Contribution

It systematically explores lightweight finetuning and prompting strategies for personalized jargon detection, demonstrating superior performance with limited data and computational resources.

Findings

LoRA-based personalization outperforms GPT-4 prompting by 21.4% in F1 score.

Achieves comparable results with only 10% of annotated data.

First systematic study of resource-efficient personalization for jargon detection.

Abstract

Personalizing jargon detection and explanation is essential for making technical documents accessible to readers with diverse disciplinary backgrounds. However, tailoring models to individual users typically requires substantial annotation efforts and computational resources due to user-specific finetuning. To address this, we present a systematic study of personalized jargon detection, focusing on methods that are both efficient and scalable for real-world deployment. We explore two personalization strategies: (1) lightweight finetuning using Low-Rank Adaptation (LoRA) on open-source models, and (2) personalized prompting, which tailors model behavior at inference time without retaining. To reflect realistic constraints, we also investigate semi-supervised approaches that combine limited annotated data with self-supervised learning from users' publications. Our personalized LoRA model outperforms GPT-4 with contextual prompting by 21.4% in F1 score and exceeds the best performing oracle baseline by 8.3%. Remarkably, our method achieves comparable performance using only 10% of the annotated training data, demonstrating its practicality for resource-constrained settings. Our study offers the first work to systematically explore efficient, low-resource personalization of jargon detection using open-source language models, offering a practical path toward scalable, user-adaptive NLP system.