The close papers a draft on “Grounding Answers in Retrieved Passages: A Study of Hallucinations in Retrieval-Augmented Language Models” does not cite

A real example: what you get

30 close papers the draft does not cite, 11 that a referee would expect.

• Where to cite each one, section by section
• How to position your work against each one
• What each one is to your draft: same question, competing method, earlier result, data...
• 246 papers screened in journals and preprints

The first results

1. Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering Gautier Izacard, Édouard Grave · 2021 This paper establishes the effectiveness of passage-conditioned generative open-domain QA on Natural Questions and TriviaQA. It evaluates answer performance and evidence aggregation rather than sentence-level support.
2. The Power of Noise: Redefining Retrieval for RAG Systems Florin Cuconasu, Giovanni Trappolini, Federico Siciliano, Simone Filice, Cesare Campagnano et al. · 2024 This paper directly examines how retrieved passage relevance affects RAG performance and reports harm from highly ranked but irrelevant passages. That finding closely relates to the draft's loss of grounding benefits under entity-mismatched retrieval, although the reported outcome is accuracy …
3. Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, Hannaneh Hajishirzi · 2023 Self-RAG addresses factuality through adaptive retrieval and reflection on both passages and generated answers. Its treatment of irrelevant retrieval and evidence use directly overlaps the draft's explanation of residual unsupported statements.
4. Disentangling Faithfulness Hallucinations in Retrieval-Augmented Generation: A Systematic Benchmark and Analysis Chandana Sree Mala, Gizem Gezici, Fosca Giannotti · 2026 This paper directly studies evidence-unsupported generation and separates retrieval errors from generation errors. It closely overlaps the draft's central question, while covering different datasets, model scales, and retrieval strategies.
5. Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis Chandana Sree Mala, Gizem Gezici, Fosca Giannotti · 2025 This preprint explicitly evaluates the relationship between retriever effectiveness and hallucination reduction, comparing sparse, dense, and hybrid retrieval. It overlaps the draft's central concern but studies retrieval alternatives rather than primarily comparing retrieval with no retrieval.
6. Retrieval Augmented Generation: A Simplified Approach to Its Operation and Understanding Achira Basu, Udit Kumar Chakraborty, Nandan Banerji · 2025 This study also evaluates RAG for QA and compares it with BART, reporting improved factual accuracy. It is a close empirical precedent, although the abstract does not establish an equivalent sentence-entailment evaluation.
7. When Attention Escapes: Diagnosing and Mitigating Hallucination in Retrieval-Augmented Generation Wencheng Su, Qinglong Wang, Zhongjie Ba, Zhixuan Chu, Zhenyu Zhong et al. · 2026 This paper directly investigates hallucinations caused by generators underusing retrieved evidence. It offers a mechanistic diagnosis and intervention relevant to the generator-side failure raised in the draft.
8. Hallucination Reduction in Large Language Models with Retrieval-Augmented Generation Using Wikipedia Knowledge Jason Kirchenbauer, Caleb Barns · 2024 This study also examines hallucination reduction through Wikipedia-based RAG and reports gains in response quality. It is a close precedent, although it uses Mistral and does not describe the draft's entailment metric or entity-mismatch test.
9. Rowen: Adaptive Retrieval-Augmented Generation for Hallucination Mitigation in LLMs Hanxing Ding, Liang Pang, Zihao Wei, Huawei Shen, Xueqi Cheng · 2024 Rowen explicitly recognizes that irrelevant retrieved information can introduce hallucinations, closely matching the draft's concern about related but incorrect entities. It proposes adaptive retrieval to balance external evidence against parametric knowledge.
10. Natural Questions: A Benchmark for Question Answering Research Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh et al. · 2019 This paper introduces Natural Questions, the benchmark on which the draft reports its central result. It is the primary dataset reference rather than a competing hallucination study.

20 more results hidden

See a complete example, explained step by step →