Abstract
Topic modeling has become a popular approach to identify semantic structures in text corpora. Despite its wide applications, interpreting the outputs of topic models remains challenging. This paper presents an initial study regarding a new approach to better understand this output, leveraging the large language model ChatGPT. Our approach is built on a three-stage process where we first use topic modeling to identify the main topics in the corpus. Then, we ask a domain expert to assign themes to these topics and prompt ChatGPT to generate human-readable summaries of the topics. Lastly, we compare the human- and machine-produced interpretations. The domain expert found half of ChatGPT’s descriptions useful. This explorative work demonstrates ChatGPT’s capability to describe topics accurately and provide useful insights if prompted accurately.
| Original language | English |
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| Number of pages | 7 |
| Publication status | Published - 2023 |
| Event | The 20th World Congress of the International Fuzzy Systems Association - Daegu, Korea, Republic of Duration: 20 Aug 2023 → 24 Aug 2023 Conference number: 20 https://ifsa2023.org/ |
Conference
| Conference | The 20th World Congress of the International Fuzzy Systems Association |
|---|---|
| Abbreviated title | IFSA |
| Country/Territory | Korea, Republic of |
| City | Daegu |
| Period | 20/08/23 → 24/08/23 |
| Internet address |
Keywords
- Topic Modeling
- Large Language Models
- ChatGPT
- Electronic Health Records
- Fuzzy Topic Models
- Prompt Engineering