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
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
1d ago
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
Aug 24
EnSI-RAG: Entity-Structure-Indexed Retrieval-Augmented Generation for Long-Document Question Answering
arXiv:2608.21252v1 Announce Type: cross Abstract: Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationshi...
arXiv Computation and Language
Aug 28
Assessing the Downstream Utility of Evidence-Aware Retrieval in RAG
The paper investigates whether incorporating an evidence-support signal into retrieval evaluation for retrieval‑augmented generation (RAG) improves downstream decision‑making. Across multiple benchmarks and a TREC RAG 2025 setting, the evidence signal alters retriever rankings but its benefits vary: it does not consistently enhance retriever training, its usefulness for system selection depends on generator instructions, and it does not reliably predict answer quality on unseen topics. Human filtering of evidence‑rich passages preserves useful content, yet evaluators disagree on whether this improves final answers, indicating that evidence‑aware evaluation alone does not guarantee better downstream outcomes.
arXiv AI
Jun 30
XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
arXiv:2412. 15529v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output is not only contextually relevant but also accurate and current.