Summary of Blowfish: Topological and Statistical Signatures For Quantifying Ambiguity in Semantic Search, by Thomas Roland Barillot and Alex De Castro
Blowfish: Topological and statistical signatures for quantifying ambiguity in semantic search
by Thomas Roland Barillot, Alex De Castro
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper presents evidence for topological signatures of ambiguity in sentence embeddings, which can be used for ranking and explanation purposes in vector search and Retrieval Augmented Generation (RAG) systems. The authors define ambiguity and design an experiment using a proprietary dataset to test the signatures while controlling for confounding factors. Results show that ambiguous queries have different distributions of homology features compared to clear queries, suggesting increased manifold complexity and/or approximately discontinuous embedding submanifolds. The findings are proposed as a new strategy for scoring semantic similarities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can better understand the meaning of sentences by finding patterns in their “word vector” representations. Researchers created a special dataset and tested different ways of asking questions to see if they could find clues about what makes some sentences more ambiguous than others. They found that when you ask bigger questions against smaller answers, it’s like looking at a puzzle from different angles – the pieces fit together differently. This discovery can help computers get better at understanding natural language and generate more accurate text. |
Keywords
» Artificial intelligence » Embedding » Rag » Retrieval augmented generation
