Summary of Comparative Study Of Domain Driven Terms Extraction Using Large Language Models, by Sandeep Chataut et al.
Comparative Study of Domain Driven Terms Extraction Using Large Language Models
by Sandeep Chataut, Tuyen Do, Bichar Dip Shrestha Gurung, Shiva Aryal, Anup Khanal, Carol Lushbough, Etienne Gnimpieba
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper reviews keyword extraction methods, focusing on the application of Large Language Models (LLMs) such as Llama2-7B, GPT-3.5, and Falcon-7B in natural language processing. The authors employ a custom Python package to interface with these models, leveraging them for keyword extraction. A key challenge addressed is prompt engineering’s impact on LLMs’ performance in extracting keywords accurately. The study evaluates the performance of these models using the Jaccard similarity index, showcasing their varying scores on the Inspec and PubMed datasets. This research highlights the significance of LLMs in keyword extraction, shedding light on their potential, limitations, and optimization techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how computers can help us find important words in big piles of text data. They use special computer models called Large Language Models to do this job. The authors test these models on two different sets of texts and see which one does the best job. They also talk about a tricky problem with these models, where they might make mistakes by inventing new information that’s not even in the original text. This research shows how computers can be useful for finding key words in big datasets, but it also highlights some challenges that need to be solved. |
Keywords
» Artificial intelligence » Gpt » Natural language processing » Optimization » Prompt
