Summary of Context-aware Clustering Using Large Language Models, by Sindhu Tipirneni et al.
Context-Aware Clustering using Large Language Models
by Sindhu Tipirneni, Ravinarayana Adkathimar, Nurendra Choudhary, Gaurush Hiranandani, Rana Ali Amjad, Vassilis N. Ioannidis, Changhe Yuan, Chandan K. Reddy
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 proposed CACTUS approach leverages open-source Large Language Models (LLMs) for efficient and effective supervised clustering of entity subsets, particularly focusing on text-based entities. The model captures context via a scalable inter-entity attention mechanism and introduces an augmented triplet loss function tailored for supervised clustering. To improve generalization, the paper introduces a self-supervised clustering task based on text augmentation techniques. Experimental results demonstrate that CACTUS significantly outperforms existing unsupervised and supervised baselines under various external clustering evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CACTUS is a new way to group similar texts together using language models. It uses open-source models that are less expensive and faster than powerful closed-source models. The approach captures the meaning of text by paying attention to how words relate to each other. It also learns from labeled data to improve its results. By comparing it to other methods, the paper shows that CACTUS is better at grouping texts together. |
Keywords
» Artificial intelligence » Attention » Clustering » Generalization » Self supervised » Supervised » Triplet loss » Unsupervised
