mlsanityData to 14 Oct 2026

Lightweight Relevance Grader in RAG

arXiv 2506.14084 ↗First posted 17 Jun 2025cs.AIcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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 at https://github.com/taeheej/Lightweight-Relevance-Grader-in-RAG.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 13 of its 17 references that are in the map. First posted on arXiv 17 Jun 2025.

Builds on

13 of 17 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 12

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 861
  5. 2024Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Surveycited by 656
  6. 2023Instruction Tuning for Large Language Models: A Surveycited by 623
  7. 2024Large Language Models: A Surveycited by 525
  8. 2023Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessmentcited by 230
  9. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 226
  10. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 202
  11. 2024Don't Forget to Connect! Improving RAG with Graph-based Rerankingcited by 49
  12. 2018Approximate Nearest Neighbor Search in High Dimensionscited by 47

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  1. 2025Is Relevance Propagated from Retriever to Generator in RAG?
  2. 2026Progressive Searching for Retrieval in RAG
  3. 2024DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering
  4. 2024RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
  5. 2024ARAGOG: Advanced RAG Output Grading
  6. 2024Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
  7. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
  8. 2024Better RAG using Relevant Information Gain