← Back to all news
arXiv Computation and Language By Aviral Joshi, Hanoz Bhathena, Max Nelson, Saket Sharma

Re:CAP - Auditing Retrieval Coverage in Production RAG Pipelines

Read the original on arXiv Computation and Language →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv AI
Jul 1

RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora

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.

By Hanjun Cho, Jay-Yoon Lee
llmsragbenchmarks
More like this →
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.

By Utshab Kumar Ghosh, Debayan Mukhopadhyay, Shubham Chatterjee
ragbenchmarkssafety
More like this →
arXiv Computation and Language
Sep 3

Incremental Pooled LLM Evaluation for Cost-Effective Retrieval Model Selection

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.

By Max Nelson, Hanoz Bhathena, Aviral Joshi, Saket Sharma
llmsragbenchmarks
More like this →