Lightweight Relevance Grader in RAG

Retrieval-Augmented Generation (RAG) addresses limitations of large language models (LLMs) by leveraging a vector database to provide more accurate and up-to-date information. When a user submits a query, RAG executes a vector search to find relevant documents, which are then used to generate a response. However, ensuring the relevance of retrieved documents with a query would be a big challenge. To address this, a secondary model, known as a relevant grader, can be served to verify its relevance. To reduce computational requirements of a relevant grader, a lightweight small language model is preferred. In this work, we finetuned llama-3.2-1b as a relevant grader and achieved a significant increase in precision from 0.1301 to 0.7750. Its precision is comparable to that of llama-3.1-70b. Our code is available atthis https URL.
View on arXiv@article{jeong2025_2506.14084, title={ Lightweight Relevance Grader in RAG }, author={ Taehee Jeong }, journal={arXiv preprint arXiv:2506.14084}, year={ 2025 } }