# Comparative Study of Domain Driven Terms Extraction Using Large Language Models

arXiv: https://arxiv.org/abs/2404.02330 · HTML: https://mlsanity.com/paper/2404.02330/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks](https://mlsanity.com/paper/2303.00293/) (2023)
- [Further Generalizations of the Jaccard Index](https://mlsanity.com/paper/2110.09619/) (2021)
- [Semantic Sensitive TF-IDF to Determine Word Relevance in Documents](https://mlsanity.com/paper/2001.09896/) (2020)

## Built on by (most cited first, 5 of 5)

- [Leveraging Multi-AI Agents for Cross-Domain Knowledge Discovery](https://mlsanity.com/paper/2404.08511/) (2024)
- [Key Algorithms for Keyphrase Generation: Instruction-Based LLMs for Russian Scientific Keyphrases](https://mlsanity.com/paper/2410.18040/) (2024)
- [IHC-LLMiner: Automated extraction of tumour immunohistochemical profiles from PubMed abstracts using large language models](https://mlsanity.com/paper/2504.00748/) (2025)
- [A Hybrid Framework for Subject Analysis: Integrating Embedding-Based Regression Models with Large Language Models](https://mlsanity.com/paper/2507.22913/) (2025)
- [Construction of a Battery Research Knowledge Graph using a Global Open Catalog](https://mlsanity.com/paper/2604.20241/) (2026)

## Abstract (arXiv)

Keywords play a crucial role in bridging the gap between human understanding and machine processing of textual data. They are essential to data enrichment because they form the basis for detailed annotations that provide a more insightful and in-depth view of the underlying data. Keyword/domain driven term extraction is a pivotal task in natural language processing, facilitating information retrieval, document summarization, and content categorization. This review focuses on keyword extraction methods, emphasizing the use of three major Large Language Models(LLMs): Llama2-7B, GPT-3.5, and Falcon-7B. We employed a custom Python package to interface with these LLMs, simplifying keyword extraction. Our study, utilizing the Inspec and PubMed datasets, evaluates the performance of these models. The Jaccard similarity index was used for assessment, yielding scores of 0.64 (Inspec) and 0.21 (PubMed) for GPT-3.5, 0.40 and 0.17 for Llama2-7B, and 0.23 and 0.12 for Falcon-7B. This paper underlines the role of prompt engineering in LLMs for better keyword extraction and discusses the impact of hallucination in LLMs on result evaluation. It also sheds light on the challenges in using LLMs for keyword extraction, including model complexity, resource demands, and optimization techniques.