🤖 AI Summary
To address the significant degradation in retrieval accuracy of vector similarity search under complex semantic queries—such as those involving constraints, negation, or abstract concepts—this paper proposes a two-stage retrieval framework: an efficient initial retrieval using approximate nearest neighbor (ANN) algorithms (e.g., FAISS), followed by context-aware fine-grained re-ranking powered by large language models (LLMs). Distinct from prior approaches, our work is the first to deeply integrate LLMs into the vector search pipeline, leveraging customized prompt engineering and a structured evaluation framework to achieve precise semantic understanding of complex queries while maintaining millisecond-scale latency. Experimental results across multiple structured benchmarks demonstrate that our method improves accuracy by 32% on average over baseline vector-only search.
📝 Abstract
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