Summary of Information-theoretic Generative Clustering Of Documents, by Xin Du et al.
Information-Theoretic Generative Clustering of Documents
by Xin Du, Kumiko Tanaka-Ishii
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR); Information Theory (cs.IT)
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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 introduces generative clustering (GC), a novel approach for clustering documents using texts generated by large language models (LLMs) instead of original documents. By defining similarity between two documents through the KL divergence, GC leverages probability distributions from LLMs to cluster documents rigorously. The authors also propose an importance sampling-based algorithm for clustering and demonstrate its effectiveness. Notably, GC outperforms previous methods in various benchmarks, achieving state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called “large language models” to help group similar documents together. Normally, we would compare the documents themselves to figure out how alike they are. But this new method, called generative clustering, compares texts that these special computers generated instead! This lets us use a more precise way of measuring similarity between documents. The authors also came up with a new algorithm for grouping documents based on their importance. They tested it and showed that it works really well. |
Keywords
» Artificial intelligence » Clustering » Probability
