mlsanityData to 14 Oct 2026

Quality analysis and evaluation prediction of RAG retrieval based on machine learning algorithms

arXiv 2511.19481 ↗First posted 22 Nov 2025cs.LGcited by 0

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

Abstract (arXiv)

With the rapid evolution of large language models, retrieval enhanced generation technology has been widely used due to its ability to integrate external knowledge to improve output accuracy. However, the performance of the system is highly dependent on the quality of the retrieval module. If the retrieval results have low relevance to user needs or contain noisy information, it will directly lead to distortion of the generated content. In response to the performance bottleneck of existing models in processing tabular features, this paper proposes an XGBoost machine learning regression model based on feature engineering and particle swarm optimization. Correlation analysis shows that answer_quality is positively correlated with doc_delevance by 0.66, indicating that document relevance has a significant positive effect on answer quality, and improving document relevance may enhance answer quality; The strong negative correlations between semantic similarity, redundancy, and diversity were -0.89 and -0.88, respectively, indicating a tradeoff between semantic similarity, redundancy, and diversity. In other words, as the former two increased, diversity significantly decreased. The experimental results comparing decision trees, AdaBoost, etc. show that the VMD PSO BiLSTM model is superior in all evaluation indicators, with significantly lower MSE, RMSE, MAE, and MAPE compared to the comparison model. The R2 value is higher, indicating that its prediction accuracy, stability, and data interpretation ability are more outstanding. This achievement provides an effective path for optimizing the retrieval quality and improving the generation effect of RAG system, and has important value in promoting the implementation and application of related technologies.

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 7 of its 15 references that are in the map. First posted on arXiv 22 Nov 2025.

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References · 7

  1. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  2. 2025Contextual Integrity in LLMs via Reasoning and Reinforcement Learningcited by 31
  3. 2023Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systemscited by 15
  4. 2025MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledgecited by 15
  5. 2019Multivariate Variational Mode Decompositioncited by 12
  6. 2025Optimization of bi-directional gated loop cell based on multi-head attention mechanism for SSD health state classification modelcited by 4
  7. 2025GPU Memory Requirement Prediction for Deep Learning Task Based on Bidirectional Gated Recurrent Unit Optimization Transformercited by 1

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  1. 2024DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering
  2. 2024ARAGOG: Advanced RAG Output Grading
  3. 2024Improving Retrieval for RAG based Question Answering Models on Financial Documents
  4. 2025Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMs
  5. 2026Predicting Retrieval Utility and Answer Quality in Retrieval-Augmented Generation
  6. 2024Evaluating Retrieval Quality in Retrieval-Augmented Generation
  7. 2026Progressive Searching for Retrieval in RAG
  8. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices