Summary of Geneol: Harnessing the Generative Power Of Llms For Training-free Sentence Embeddings, by Raghuveer Thirukovalluru and Bhuwan Dhingra
GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence Embeddings
by Raghuveer Thirukovalluru, Bhuwan Dhingra
First submitted to arxiv on: 18 Oct 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 A novel method called GenEOL leverages large language models (LLMs) to directly embed text without training. Unlike existing methods that optimize prompts, GenEOL harnesses the generative capabilities of LLMs to transform sentences while preserving meaning. This approach generates diverse embeddings, which are then aggregated to improve overall sentence representation. GenEOL outperforms previous methods on the sentence semantic text similarity (STS) benchmark by an average of 2.85 points across various LLMs. It also excels in clustering, reranking, and pair-classification tasks from the MTEB benchmark. Furthermore, GenEOL stabilizes representation quality across LLM layers and remains robust to perturbations in embedding prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to describe text without needing special training. A new method called GenEOL does just that by using powerful language models. These models can change sentences in many ways while keeping the same meaning. GenEOL takes these changes and combines them to create a better description of the original sentence. This approach works better than previous methods on several tasks, like comparing how similar two texts are. It also helps with grouping text into categories and finding the best matches between pairs of texts. Plus, GenEOL’s results don’t change much even if you make small changes to the way it describes text. |
Keywords
» Artificial intelligence » Classification » Clustering » Embedding
