ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2501.11929v1
• Date: Tue, 21 Jan 2025 07:07:58 GMT
• Title: ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented Generation
• Authors: Peter Devine,
• Abstract summary: We introduce the Automatic Local Fine Tuning of Retrieval Augmented Generation models (ALoFTRAG) framework. ALoFTRAG improves the accuracy of RAG systems on a given domain by training LLMs without manually labeled data or using larger teacher models. Our results demonstrate that ALoFTRAG offers a practical, cost-effective, and data-secure solution for improving RAG accuracy.
• Score: 3.8073142980733
• License:
• Abstract: 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.

Related papers

• Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA [0.0]
  Recent advancements in Generative AI have significantly improved the efficiency and adaptability of natural language processing (NLP) systems. This paper presents a large-scale empirical evaluation of RAG, LoRA, and DoRA, with model fine-tuning and generation performance assessed on 20,000 FAQ-based queries. DoRA achieves the highest accuracy (90.1%), relevance score (0.88), and lowest latency (110 ms per query), outperforming both LoRA and RAG in real-world, domain-specific generative AI applications.
  arXiv  Detail & Related papers  (2025-02-14T17:38:25Z)
• Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs [76.40876036912537]
  Large Language Models (LLMs) demonstrate strong few-shot adaptability without requiring fine-tuning. Current Visual Foundation Models (VFMs) require explicit fine-tuning with sufficient tuning data. We propose a framework, LoRA Recycle, that distills a meta-LoRA from diverse pre-tuned LoRAs with a meta-learning objective.
  arXiv  Detail & Related papers  (2024-12-03T07:25:30Z)
• Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation [73.9145653659403]
  We show that Generative Error Correction models struggle to generalize beyond the specific types of errors encountered during training. We propose DARAG, a novel approach designed to improve GEC for ASR in in-domain (ID) and OOD scenarios. Our approach is simple, scalable, and both domain- and language-agnostic.
  arXiv  Detail & Related papers  (2024-10-17T04:00:29Z)
• Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation [13.120801609024147]
  retrieval augmented generation (RAG) has been shown to enhance factuality of large language model (LLM) outputs. RAG inputs are more complex than most datasets used for training NLI models. We introduce Automatic Generative Domain Adaptation (Auto-GDA) to enable unsupervised domain adaptation.
  arXiv  Detail & Related papers  (2024-10-04T14:21:27Z)
• RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation [8.377398103067508]
  We introduce RAG Foundry, an open-source framework for augmenting large language models for RAG use cases. RAG Foundry integrates data creation, training, inference and evaluation into a single workflow. We demonstrate the framework effectiveness by augmenting and fine-tuning Llama-3 and Phi-3 models with diverse RAG configurations.
  arXiv  Detail & Related papers  (2024-08-05T15:16:24Z)
• AutoScale: Automatic Prediction of Compute-optimal Data Composition for Training LLMs [61.13296177652599]
  This paper demonstrates that the optimal composition of training data from different domains is scale-dependent. We introduce *AutoScale*, a novel, practical approach for optimizing data compositions at potentially large training data scales. Our evaluation on GPT-2 Large and BERT pre-training demonstrates *AutoScale*'s effectiveness in improving training convergence and downstream performance.
  arXiv  Detail & Related papers  (2024-07-29T17:06:30Z)
• Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models [0.8399688944263842]
  Large Language Models (LLMs) have the capability to understand and generate human-like text from input queries. This study extends this concept to the integration of LLMs within Retrieval-Augmented Generation (RAG) pipelines. We evaluate the impact of fine-tuning on the LLMs' capacity for data extraction and contextual understanding.
  arXiv  Detail & Related papers  (2024-06-17T04:35:17Z)
• Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases [9.478012553728538]
  We propose an end-to-end system design towards utilizing Retrieval Augmented Generation (RAG) to improve the factual accuracy of Large Language Models (LLMs) Our system integrates RAG pipeline with upstream datasets processing and downstream performance evaluation. Our experiments demonstrate the system's effectiveness in generating more accurate answers to domain-specific and time-sensitive inquiries.
  arXiv  Detail & Related papers  (2024-03-15T16:30:14Z)
• CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration [59.48235003469116]
  We show that data augmentation consistently enhances OOD performance. We also show that CF augmented models which are easier to calibrate also exhibit much lower entropy when assigning importance.
  arXiv  Detail & Related papers  (2023-09-14T16:16:40Z)
• Scaling Relationship on Learning Mathematical Reasoning with Large Language Models [75.29595679428105]
  We investigate how the pre-training loss, supervised data amount, and augmented data amount influence the reasoning performances of a supervised LLM. We find that rejection samples from multiple models push LLaMA-7B to an accuracy of 49.3% on GSM8K which outperforms the supervised fine-tuning (SFT) accuracy of 35.9% significantly.
  arXiv  Detail & Related papers  (2023-08-03T15:34:01Z)
• Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training [55.824641135682725]
  Domain adaptation experiments using WSJ as a source domain and TED-LIUM 3 as well as SWITCHBOARD show that up to 80% of the performance of a system trained on ground-truth data can be recovered.
  arXiv  Detail & Related papers  (2020-11-26T18:51:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.