---
title: "Who cites Understanding Retrieval — 24 citing papers"
description: "24 papers of the Elamur Research library cite «Understanding Retrieval Augmentation for Long-Form Question Answering», found in their…"
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---
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# Who cites «Understanding Retrieval Augmentation for Long-Form Question Answering»

24 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «Understanding Retrieval Augmentation for Long-Form Question Answering»](https://md.research.elamur.ai/free/en/papers/chen-2023-understanding-retrieval-augmentation-long-form/)

1. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
2. [**Retrieval-Augmented Generation for Large Language Models: A Survey arXiv:2312.10997**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,663 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
3. **[MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liyan Tang, Philippe Laban, Greg Durrett*2024 · 166 citations*
4. [**Retrieval-Augmented Generation with Graphs (GraphRAG) arXiv:2501.00309**Haoyu Han, Yu Wang, Harry Shomer, et al.*2025 · 87 citations*](https://md.research.elamur.ai/free/en/papers/han-2025-retrieval-augmented-generation-graphs-graphrag/)
5. **[Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems](https://md.research.elamur.ai/free/en/papers/laban-2024-summary-haystack-challenge-long-context/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Philippe Laban, Alexander Fabbri, Caiming Xiong, et al.*2024 · 69 citations*
6. **[Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization](https://md.research.elamur.ai/free/en/papers/huang-2025-improving-contextual-faithfulness-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lei Huang, Xiaocheng Feng, Weitao Ma, et al.*2025 · 11 citations*
7. **[FaithLens: Detecting and Explaining Faithfulness Hallucination](https://md.research.elamur.ai/free/en/papers/si-2026-faithlens-detecting-explaining-faithfulness-hallucination/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shuzheng Si, Qingyi Wang, Haozhe Zhao, et al.*2026 · 1 citation*
8. **[Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies](https://md.research.elamur.ai/free/en/papers/ye-2026-teaching-language-models-check-grounded/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxuan Ye, Raul Santos-Rodriguez, Edwin Simpson*2026*

16 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/chen-2023-understanding-retrieval-augmentation-long-form/) — Understanding Retrieval Augmentation for Long-Form Question Answering