mlsanityData to 14 Oct 2026

Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions

arXiv 2604.12138 ↗First posted 13 Apr 2026cs.AI cs.CL cs.IRcited by 1

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems are built on an unexamined assumption - that queries have correct answers and retrieval should converge toward them. This position paper argues that this creates a factual bias where RAG systems optimize for reducing epistemic uncertainty while ignoring the aleatoric uncertainty, inherent in opinion-rich content. The consequences go beyond technical limitations- due to risk of minority voice erasure and risk of opinion manipulation. To address this, we formalize opinion-aware retrieval through uncertainty quantification and derive a unified objective using the Wasserstein distance. As an existence proof, we present Opinion-Aware RAG (O-RAG), which enriches documents with LLM-extracted, entity-linked opinion metadata before indexing. Across e-commerce seller forums and public hotel reviews, O-RAG reduces Wasserstein distance to corpus-level sentiment distributions by 18-48%, and human evaluators preferred its responses 79.2% of the time. We close with a research agenda for opinion-aware RAG.

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 63 of its 81 references that are in the map. First posted on arXiv 13 Apr 2026.

Builds on

63 of 81 references.

Closest ancestors · 10

Same direction, newest first

  1. 2025Fairness Testing in Retrieval-Augmented Generation: How Small Perturbations Reveal Bias in Small Language Models30 Sep
  2. 2025Conflict-Aware Soft Prompting for Retrieval-Augmented Generation21 Aug
  3. 2025MAGIC: A Multi-Hop and Graph-Based Benchmark for Inter-Context Conflicts in Retrieval-Augmented Generation29 Jul
  4. 2025Large Language Models in Argument Mining: A Survey19 Jun
  5. 2025FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation10 Jun
  6. 2025DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs10 Jun
  7. 2025Contradiction Detection in RAG Systems: Evaluating LLMs as Context Validators for Improved Information Consistency31 Mar
  8. 2025Evaluating the Effect of Retrieval Augmentation on Social Biases24 Feb
  9. 2025Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals22 Feb
  10. 2024Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments14 Jun

Other references · 53

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  3. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  5. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  6. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  7. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  8. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
  9. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  10. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  11. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  12. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
38 more
  1. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  2. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  3. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  4. 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
  5. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 916
  6. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 752
  7. 2017The NarrativeQA Reading Comprehension Challengecited by 597
  8. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  9. 2019ELI5: Long Form Question Answeringcited by 575
  10. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 573
  11. 2021A Dataset of Information-Seeking Questions and Answers Anchored in Research Paperscited by 486
  12. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  13. 2021QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarizationcited by 365
  14. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 354
  15. 2022ASQA: Factoid Questions Meet Long-Form Answerscited by 234
  16. 2020Explainable Automated Fact-Checking for Public Health Claimscited by 211
  17. 2021QuALITY: Question Answering with Long Input Texts, Yes!cited by 208
  18. 2024Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judgescited by 205
  19. 2022Fine-tuning language models to find agreement among humans with diverse preferencescited by 189
  20. 2022Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidencecited by 144
  21. 2024RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systemscited by 117
  22. 2023CMB: A Comprehensive Medical Benchmark in Chinesecited by 115
  23. 2023Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Dutiescited by 97
  24. 2024CRAG -- Comprehensive RAG Benchmarkcited by 97
  25. 2020Stance Detection on Social Media: State of the Art and Trendscited by 95
  26. 2020SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!cited by 78
  27. 2021A Survey on Stance Detection for Mis- and Disinformation Identificationcited by 75
  28. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Surveycited by 20
  29. 2025A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challengescited by 17
  30. 2024Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awarenesscited by 12
  31. 2024Open-World Evaluation for Retrieving Diverse Perspectivescited by 11
  32. 2025Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMscited by 8
  33. 2023Neural Natural Language Processing for Long Texts: A Survey on Classification and Summarizationcited by 7
  34. 2026Hidden-in-Plain-Text: A Benchmark for Social-Web Indirect Prompt Injection in RAGcited by 5
  35. 2025Adversarial Attacks against Neural Ranking Models via In-Context Learningcited by 4
  36. 2025OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviewscited by 4
  37. 2024CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Redditcited by 3
  38. 2018YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehiclescited by 2

50 most cited of 53 All 53 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026WARP: Wasserstein-Aligned RAG for Population Opinionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Fairness-Aware Retrieval Optimization for Retrieval-Augmented Generation
  2. 2025Mitigating Bias in RAG: Controlling the Embedder
  3. 2026URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models
  4. 2026The RAT: A Unified Bayesian Model for RAG Evaluation
  5. 2026ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation
  6. 2026Evaluating RAG Reliability under Clean, Misleading, and Mixed Retrieval
  7. 2024Retrieval-Augmented Generation with Estimation of Source Reliability
  8. 2025RARE: Retrieval-Aware Robustness Evaluation for Retrieval-Augmented Generation Systems