The paper proposes an incremental pooled LLM evaluation method for selecting retrieval models in production Retrieval-Augmented Generation (RAG) systems. By having a language model judge the union of documents retrieved by current candidates and expanding the pool only with new documents from added systems, the approach reuses judgments across all systems. Experiments on four benchmarks and a financial news QA deployment show strong correlation with gold-standard rankings, high preservation of pairwise orderings, and significant cost savings—up to 4.9× lower evaluation cost and 65–80% judgment reuse.
arXiv:2604. 19047v2 Announce Type: replace-cross Abstract: Existing QA benchmarks typically assume distinct documents with minimal overlap, yet real-world retrieval-augmented generation (RAG) systems operate on corpora such as financial reports, legal codes, and patents, where information is highly redundant and documents exhibit strong inter-document similarity.
arXiv:2602.18613v2 Announce Type: replace Abstract: Standard reranking evaluations study how a reranker orders candidates returned by an upstream retriever. This setup couples ranking behavior with r...
Q2D-Web is a new large‑scale benchmark for agentic Retrieval‑Augmented Generation (RAG) systems, featuring a 190 million‑document web corpus and 70 k machine‑reformulated search queries in ten languages. It supplies three sets of relevance judgments—agent citations, production rankings, and a combined set enriched with LLM‑based labels—to evaluate first‑stage retrievers. Experiments on 13 retrievers show consistent ranking across judgment sets but significant variation across domains, languages, and query types, and demonstrate that a carefully sampled sub‑corpus can approximate full‑corpus evaluation with minimal loss in Recall@1000.
The paper evaluates how robust large language models (LLMs) are when using retrieval‑augmented generation (RAG) in practical settings. It investigates whether RAG always outperforms non‑RAG approaches, whether adding more retrieved documents helps, and whether the order of documents matters, using a benchmark of 1,891 samples across five datasets and three task categories. Experiments with 11 LLMs show generally high retrieval robustness, but performance varies by task and prompting strategy, indicating that adopting RAG should be considered on a case‑by‑case basis.
arXiv:2603. 26815v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems for financial document QA typically follow a chunk-based paradigm: documents are split into fragments, embedded, and retrieved by similarity.
The paper introduces SetwiseEvalKit, a benchmark with nine dimensions and 28K rubrics for evaluating document sets, highlighting that current rerankers only achieve up to 45% coverage and struggle with cross‑document coordination. It proposes Rubric4Setwise, a training‑free method that transforms rubric criteria into selection signals, outperforming other methods in downstream generation tasks across both short‑form and long‑form scenarios. The study demonstrates the importance of evaluating and optimizing document sets as a whole rather than independently.
The paper investigates how adding non‑relevant documents to a collection affects the performance of Information Retrieval (IR) models. It distinguishes between Multi‑Document‑Agnostic (MDA) models, which do not rely on other documents, and Multi‑Document‑Dependent (MDD) models, which do, and evaluates both on two collections with little topic overlap. Results show that all models suffer some performance loss when non‑relevant documents are added, but MDA models outperform MDD models in retrieval tasks, while both perform similarly in reranking.
Re:CAP is a reference‑free audit loop for retrieval‑augmented generation (RAG) pipelines that probes for missing documents instead of enumerating all relevant ones. It identifies covered topics, generates probing questions, retrieves candidate documents, and uses an LLM judge to keep only those that add new information. On several benchmarks, Re:CAP recovers a significant portion of gold documents that flat BM25 or hybrid retrieval misses, and human evaluation shows most of these documents add new information.
The study compares 11 retrieval configurations for long-document Retrieval-Augmented Generation (RAG), including lexical, dense, hybrid reranking, and full-document scanning methods. Using 396 QASPER questions and 80 textbook questions, the hybrid approach with Jev achieved the highest evidence coverage (94.1%) and the hybrid with Decisions yielded the best answer accuracy (0.892), despite lower evidence recovery. Full scanning offers higher coverage but incurs higher latency and cost, while reusable dense retrieval is cheaper as queries accumulate.
The paper introduces “PACE”, a training‑free framework that tackles bottlenecks in Retrieval‑Augmented Generation by frontloading evidence and adaptively budgeting reranking. It first reorders candidate documents based on marginal evidence coverage—prioritizing query‑relevant, complementary, and chain‑forming documents—providing a $(1-1/e)$ approximation guarantee. Then it dynamically adjusts the reranking budget according to the relative pressure of the reranker and the language model, improving evidence recall and reducing p95 latency in multi‑hop QA workloads.
The paper evaluates three retrieval strategies—Vector RAG, Tree Reasoning, and a new Adaptive Hybrid Retrieval (AHR)—on a small corpus of financial, legal, and medical documents, using a four‑tier query complexity benchmark and LLM‑as‑judge evaluation. Tree Reasoning achieves the highest overall score (0.900), but AHR excels on cross‑reference queries (0.850) and all methods tie on multi‑section queries (0.929). Vector RAG shows the lowest latency (1.61 s) and performs best on single multi‑document queries (0.900).
arXiv:2606. 28365v1 Announce Type: cross Abstract: RAG ingestion pipelines frequently augment search corpus index with semantic enrichment indices (e.