# De-jargonizing Science for Journalists with GPT-4: A Pilot Study

arXiv: https://arxiv.org/abs/2410.12069 · HTML: https://mlsanity.com/paper/2410.12069/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)

## Builds on (most cited first, 4 of 4)

- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [RankME: Reliable Human Ratings for Natural Language Generation](https://mlsanity.com/paper/1803.05928/) (2018)
- [Personalized Jargon Identification for Enhanced Interdisciplinary Communication](https://mlsanity.com/paper/2311.09481/) (2023)
- [Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism](https://mlsanity.com/paper/2311.06864/) (2023)

## Abstract (arXiv)

This study offers an initial evaluation of a human-in-the-loop system leveraging GPT-4 (a large language model or LLM), and Retrieval-Augmented Generation (RAG) to identify and define jargon terms in scientific abstracts, based on readers' self-reported knowledge. The system achieves fairly high recall in identifying jargon and preserves relative differences in readers' jargon identification, suggesting personalization as a feasible use-case for LLMs to support sense-making of complex information. Surprisingly, using only abstracts for context to generate definitions yields slightly more accurate and higher quality definitions than using RAG-based context from the fulltext of an article. The findings highlight the potential of generative AI for assisting science reporters, and can inform future work on developing tools to simplify dense documents.