Summary of Llm-assisted Vector Similarity Search, by Md Riyadh et al.
LLM-assisted Vector Similarity Search
by Md Riyadh, Muqi Li, Felix Haryanto Lie, Jia Long Loh, Haotian Mi, Sayam Bohra
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Retrieval (cs.IR); 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 hybrid approach combines vector similarity search with Large Language Models (LLMs) to enhance search accuracy and relevance for complex queries. The method first employs vector similarity search to shortlist potential matches, followed by an LLM for context-aware ranking of the results. Experiments on structured datasets demonstrate that the LLM-assisted approach excels in processing complex queries involving constraints, negations, or conceptual requirements. This technique improves the accuracy of search results for complex tasks without sacrificing efficiency, making it a promising solution for nuanced and conceptual queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to find similar information on computers using large language models (LLMs). Right now, searching for information is like looking through a big library: you might find what you’re looking for, but it takes time and effort. This approach helps make search results more accurate by using LLMs to understand the context of what you’re looking for. It’s like having a smart librarian help you find exactly what you need. |
