ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented Generation
To address the significant degradation in citation and answer accuracy of Retrieval-Augmented Generation (RAG) systems when deployed on emerging domain data—such as in healthcare and finance—this paper proposes an automated, local fine-tuning framework that requires neither human annotation nor large teacher models. Our method introduces an unsupervised LoRA fine-tuning paradigm grounded in synthetic data generation and multi-stage quality filtering, jointly optimizing retrieval and generation across languages. Key components include controllable synthetic data generation, multi-dimensional quality assessment, efficient LoRA parameter adaptation, and cross-lingual retrieval-generation co-optimization. Evaluated on 20 datasets spanning 26 languages, our approach achieves average improvements of +8.3% in citation accuracy and +3.0% in answer accuracy. The framework ensures domain adaptability, data privacy preservation, and low deployment overhead, substantially enhancing the reliability and generalizability of RAG systems in sensitive domains.