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DeclineCraft: A RAG Abstention Benchmark for Jointly Measuring Combined Retrieval and Parametric Knowledge

Paper recorded by Signals 4 on 2026-10-20 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-20 on arXiv · recorded by Signals 4 on 2026-10-21

Category: cs.CL · 自然语言处理 · first seen 2026-10-21

Abstract

Reliable retrieval-augmented generation (RAG) requires recognizing when neither retrieved documents nor parametric knowledge supports an answer. We introduce DeclineCraft, a dynamic abstention benchmark whose unanswerable questions target Minecraft feature additions after a corpus cut-off, with audits of possible parametric exposure. Later cut-offs support evaluation of future models. Across 27 LL

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#207 most recent of 660 cs.CL papers we have recorded · ↑ newer: Object-Oriented Harness: Evolving Agent Harnesses through Object-Orien · ↓ older: Can It Actually Work? Synthesizing Functional CAD Mechanisms
Cite this page: DeclineCraft: A RAG Abstention Benchmark for Jointly Measuring Combined Retrieval and Parametric Knowledge: the #207 most recent of 660 cs.CL papers we have recorded (as of 2026-10-20). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/declinecraft-a-rag-abstention-benchmark-for-jointly-measuring-combined-retrieval.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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