Summary of Astro-ner — Astronomy Named Entity Recognition: Is Gpt a Good Domain Expert Annotator?, by Julia Evans et al.
Astro-NER – Astronomy Named Entity Recognition: Is GPT a Good Domain Expert Annotator?
by Julia Evans, Sameer Sadruddin, Jennifer D’Souza
First submitted to arxiv on: 4 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study addresses the scarcity of labeled data for NER models in scholarly domains by leveraging predictions from a fine-tuned LLM model to aid non-domain experts in annotating scientific entities within astronomy literature. The goal is to uncover whether such a collaborative process can approximate domain expertise. The results show moderate agreement between a domain expert and the LLM-assisted non-experts, as well as fair agreement between the domain expert and the LLM model’s predictions. Additionally, the study compares the performance of fine-tuned and default LLMs on this task, introducing a specialized scientific entity annotation scheme for astronomy validated by a domain expert. The approach focuses exclusively on scientific entities relevant to research themes, making publicly available a dataset containing 5,000 annotated astronomy article titles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in computer science called NER (Named Entity Recognition). It’s hard to find labeled data to train models for recognizing important words like names and places in scholarly articles. The researchers tried something new: they used a special AI model (LLM) that was already good at understanding text, but not domain-specific, to help non-experts annotate scientific entities in astronomy articles. They compared the results with those of a real expert in astronomy and found some agreement. They also tested two different versions of the LLM model and introduced a new way to label important words relevant to research themes. The result is a big dataset of labeled text that anyone can use. |
Keywords
» Artificial intelligence » Named entity recognition » Ner
