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AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline

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arxiv 2410.20878 v1 pith:7MQ44HXC submitted 2024-10-28 cs.CL

Dongkyu Kim , Byoungwook Kim , Donggeon Han , Matouš Eibich This is my paper · ORCID
classification cs.CL
keywords autoragmodulesdatasetdatasetsframeworkgenerationgithubresults
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Using LLMs (Large Language Models) in conjunction with external documents has made RAG (Retrieval-Augmented Generation) an essential technology. Numerous techniques and modules for RAG are being researched, but their performance can vary across different datasets. Finding RAG modules that perform well on specific datasets is challenging. In this paper, we propose the AutoRAG framework, which automatically identifies suitable RAG modules for a given dataset. AutoRAG explores and approximates the optimal combination of RAG modules for the dataset. Additionally, we share the results of optimizing a dataset using AutoRAG. All experimental results and data are publicly available and can be accessed through our GitHub repository https://github.com/Marker-Inc-Korea/AutoRAG_ARAGOG_Paper .

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. C$^2$KV: Compressed and Composable KV Cache Reuse for Efficient LLM Inference

    cs.CL 2026-07 conditional novelty 6.0 of 10

    C2KV trains a sidecar extractor to produce compressed, position-agnostic KV cache segments that can be concatenated at inference time, reducing prefill, storage, and memory-bandwidth costs without modifying the frozen...

  2. VulRTex: A Reasoning-Guided Approach to Identify Vulnerabilities from Rich-Text Issue Report

    cs.SE 2025-09 conditional novelty 6.0 of 10

    A retrieval-augmented LLM approach that identifies vulnerability-related issue reports and CWE types from screenshots and code snippets, improving F1 by 11 points and AUPRC by 20 points over baselines.

  3. FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation

    cs.CL 2025-06 conditional novelty 5.0 of 10

    FlexRAG is a modular, open-source RAG framework with text, multimodal, and web retrieval, plus evaluation tools and efficient memory-mapped indexing.

  4. Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization

    cs.CL 2025-05 reject novelty 5.0 of 10

    RoleRAG tunes only role-token embeddings on a frozen LLM to run six RAG sub-tasks, reporting improved QA accuracy, but with inconsistent headline numbers and no significance tests.

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