arXiv AI
Automating Multi-Hop RAG Evaluation via TRIAD: From Context Extraction to Validated Dataset Generation
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arXiv AI
Jun 12
DCD: Domain-Oriented Design for Controlled Retrieval-Augmented Generation
arXiv:2604. 07590v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) is widely used to ground large language models in external knowledge sources.
arXiv AI
1d ago
RAG-NIMBF: A Non-Invasive, Modular Benchmarking Framework for Automated RAG Configuration Analysis
arXiv:2610.27816v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become the dominant paradigm for grounding the outputs of large language models (LLM) in external knowledge....
arXiv AI
Oct 2
Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning
The paper surveys recent advances in Retrieval Augmented Generation (RAG), a technique that integrates external retrieval into language model generation to reduce hallucinations and keep knowledge current. It introduces a four‑axis taxonomy—efficiency, defense, interactivity, and reasoning—to organize contemporary RAG research, covering retrieval methods, fusion strategies, embedding optimizations, and reinforcement learning policies. The survey also reviews evaluation practices, domain‑specific applications, and architectural variants, while highlighting ongoing challenges such as retrieval quality, reliability, domain adaptation, scalability, and explainability.
arXiv AI
Jul 21
A Survey on Knowledge-Oriented Retrieval-Augmented Generation
arXiv:2503. 10677v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has gained significant attention in recent years for its potential to enhance natural language understanding and generation by combining large-scale retrieval systems with generative models.
arXiv AI
Sep 3
PRO-Step: Step-level Process Reward Optimization for Retrieval-Augmented Generation
PRO-Step introduces a step‑level process reward optimization framework for Retrieval‑Augmented Generation (RAG) that evaluates both logical validity and evidential grounding at each reasoning step. By training a generative Preference‑Based Reward Model (PRM) and using PRM‑guided value tree search to create preference pairs, the method optimizes the policy through step‑level Direct Preference Optimization. Experiments on single and multi‑hop QA benchmarks show that PRO‑STEP achieves the best average EM and F1 scores across five datasets.
arXiv AI
Oct 7
Rethinking Knowledge Retrieval for Generation: A Survey on RAG Architectures and Applications
arXiv:2610.01936v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across natural language tasks but remain fundamentally limited by...