Summary of Generative Ai For Automatic Topic Labelling, by Diego Kozlowski et al.
Generative AI for automatic topic labelling
by Diego Kozlowski, Carolina Pradier, Pierre Benz
First submitted to arxiv on: 13 Aug 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 The paper proposes assessing the reliability of three language models (LLMs) for topic labeling in scientific fields. The authors generate topics from a dataset of biology articles authored by Swiss professors between 2008 and 2020, using BERTopic. They evaluate the output of GPT-4o, GPT-4 mini, and flan models quantitatively and qualitatively, finding that both GPT models accurately label topics with precise keywords. The study suggests that three-word labels are most effective in capturing research topic complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well language models can help us understand scientific trends. Researchers use these models to analyze big groups of papers and identify important topics. The authors tested three models, called flan, GPT-4o, and GPT-4 mini, on a large dataset of biology articles written by Swiss professors. They found that two of the models did a great job of identifying and labeling these topics. This study shows us that language models can be a useful tool in understanding scientific research. |
Keywords
» Artificial intelligence » Gpt
