Human-interpretable clustering of short text using large language models
Clustering short text is a difficult problem, owing to the low word co-occurrence between short text documents. This work shows that large language models (LLMs) can overcome the limitations of traditional clustering approaches by generating embeddings that capture the semantic nuances of short text...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
The Royal Society
2025-01-01
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| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.241692 |
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