mlsanityData to 14 Oct 2026

ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented Generation

arXiv 2501.11929 ↗First posted 21 Jan 2025cs.LGcited by 3

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

Abstract (arXiv)

Retrieval Augmented Generation (RAG) systems have been shown to improve the accuracy of Large Language Model (LLM) outputs. However, these models can often achieve low accuracy when applied to new data domains. We introduce the Automatic Local Fine Tuning of Retrieval Augmented Generation models (ALoFTRAG) framework, designed to improve the accuracy of RAG systems on a given domain by training LLMs without manually labeled data or using larger teacher models. By generating and filtering synthetic training data and performing LoRA fine-tuning, ALoFTRAG improves citation and answer accuracy across 20 datasets in 26 languages by, on average, 8.3% and 3.0% respectively. Our results demonstrate that ALoFTRAG offers a practical, cost-effective, and data-secure solution for improving RAG accuracy, making it particularly applicable to sensitive domains such as healthcare and finance.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 28 of its 42 references that are in the map. First posted on arXiv 21 Jan 2025.

Builds on

28 of 42 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework2 Aug
  2. 2024The Chronicles of RAG: The Retriever, the Chunk and the Generator15 Jan
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 25

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  5. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  6. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  7. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  8. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  9. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  10. 2017The NarrativeQA Reading Comprehension Challengecited by 597
  11. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 578
  12. 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 488
13 more
  1. 2019MLQA: Evaluating Cross-lingual Extractive Question Answeringcited by 340
  2. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
  3. 2022MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Textcited by 220
  4. 2023Language Model Self-improvement by Reinforcement Learning Contemplationcited by 75
  5. 2018DRCD: a Chinese Machine Reading Comprehension Datasetcited by 57
  6. 2024Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Datacited by 52
  7. 2019Neural Arabic Question Answeringcited by 48
  8. 2019KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehensioncited by 43
  9. 2021GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrievalcited by 39
  10. 2019SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysiscited by 38
  11. 2024CaLMQA: Exploring culturally specific long-form question answering across 23 languagescited by 31
  12. 2024T-RAG: Lessons from the LLM Trenchescited by 23
  13. 2022KenSwQuAD -- A Question Answering Dataset for Swahili Low Resource Languagecited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Privacy-Preserving RAG by Concealing Sensitive Information from External LLMscited by 0
  2. 2026ReliableRAG: Combating Misinformation in Retrieval-Augmented Generation via Reliability-Guided Reasoning Chainscited by 0
  3. 2026Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

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  2. 2024Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
  3. 2024AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline
  4. 2024XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
  5. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  6. 2024Retrieval-Augmented Generation with Estimation of Source Reliability
  7. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely
  8. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research