2024/12/25 by Md Riyadh, Muqi Li, Riyadh, Md +7
Computer Science · #Advanced Computational Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Fuzzy Logic and Control Systems #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2412.18819
openalex publication_date 2024/12/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As data retrieval demands become increasingly complex, traditional search methods often fall short in addressing nuanced and conceptual queries. Vector similarity search has emerged as a promising technique for finding semantically similar information efficiently. However, its effectiveness diminishes when handling intricate queries with contextual nuances. This paper explores a hybrid approach combining vector similarity search with Large Language Models (LLMs) to enhance search accuracy and relevance. The proposed two-step solution 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 while vector similarity search alone performs well for straightforward queries, the LLM-assisted approach excels in processing complex queries involving constraints, negations, or conceptual requirements. By leveraging the natural language understanding capabilities of LLMs, this method improves the accuracy of search results for complex tasks without sacrificing efficiency. We also discuss real-world applications and propose directions for future research to refine and scale this technique for diverse datasets and use cases. Original article: https://engineering.grab.com/llm-assisted-vector-similarity-search