mlsanityData to 14 Oct 2026

Answer Presence Drives RAG Rewriting Gains

arXiv 2606.05633 ↗First posted 4 Jun 2026cs.AIcited by 1

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Retrieval-augmented QA pipelines often route retrieved passages through an LLM rewriter before a smaller reader, lifting F1 by tens of points on multi-hop benchmarks; this gain is typically credited to improved evidence quality. We ask whether that lift is causally driven by the gold answer string appearing in the rewritten context rather than by curation per se, using a controlled intervention audit. For each rewritten context we re-run the reader after one of four controlled edits to the compile output: removing the gold answer span, replacing a length-matched random non-answer span (placebo), or injecting the gold into rewrites where it was absent (at the prefix or at a midpoint sentence boundary). Across twelve completed (cell, baseline) intervention runs spanning three reader families (Qwen2.5-7B, Qwen3.5-35B, GLM-4.7), two datasets (HotpotQA, 2WikiMultihopQA), and three compiler arrangements (MA-only, MB-only, MA+verify), removing the gold answer drops reader F1 by 28 to 64 points beyond the length-matched placebo on paired textttanswer-in-compile strata, and prepending the gold into rewrites that lacked it raises F1 by +0.7 to +9.7 points in 10 of 12 (cell, baseline) combinations. A companion five-sentinel audit shows the conventional single-texttt[MASK] probe is itself sentinel-fragile: on 2Wiki it reports a +4.12~F1 ``non-leakage residual'' that flips to -3.33 to -7.81~F1 under four alternative sentinels and fails an equivalence test for three of those four (1/4~pass). We do not propose a new rewriter or mitigation; we release the intervention runner and the sentinel panel so that other rewriter-gain claims can be tested against the same standard.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 27 of its 36 references that are in the map. First posted on arXiv 4 Jun 2026.

Builds on

27 of 36 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 26

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  4. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  5. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
  6. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  7. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 1,006
  8. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  9. 2023Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formattingcited by 817
  10. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  11. 2019Learning the Difference that Makes a Difference with Counterfactually-Augmented Datacited by 439
  12. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
14 more
  1. 2021Entity-Based Knowledge Conflicts in Question Answeringcited by 356
  2. 2023NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmarkcited by 348
  3. 2021Do Prompt-Based Models Really Understand the Meaning of their Prompts?cited by 326
  4. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 281
  5. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Surveycited by 244
  6. 2022Data Contamination: From Memorization to Exploitationcited by 236
  7. 2019Show Your Work: Improved Reporting of Experimental Resultscited by 169
  8. 2024Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modelingcited by 161
  9. 2024Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Datasetcited by 160
  10. 2024Benchmarking Complex Instruction-Following with Multiple Constraints Compositioncited by 152
  11. 2020With Little Power Comes Great Responsibilitycited by 142
  12. 2020Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasetscited by 138
  13. 2024Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvementscited by 83
  14. 2022Mask More and Mask Later: Efficient Pre-training of Masked Language Models by Disentangling the [MASK] Tokencited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Evidence Interfaces Shape How Retrieval-Augmented Readers Use Support
  2. 2026CUE-R: Beyond the Final Answer in Retrieval-Augmented Generation
  3. 2026Not All Queries Need Rewriting: When Prompt-Only LLM Refinement Helps and Hurts Dense Retrieval
  4. 2026RAG-Stress: Probing the Limits of Evidence Reliance in Retrieval-Augmented Generation
  5. 2025Read the Docs Before Rewriting: Equip Rewriter with Domain Knowledge via Continual Pre-training
  6. 2026Know Before You Fetch: Calibrated Retrieval-Budget Allocation for Retrieval-Augmented Generation
  7. 2026Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation
  8. 2026Relevant but Incomplete: Referential Dangling as a Paradigm-Level Failure Mode in Hard Prompt Compression