---
title: "Who cites Ivan Stelmakh — 196 papers of our library"
description: "196 papers of the Elamur Research library cite the works of Ivan Stelmakh, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/ivan-stelmakh/cited-by/"
---
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# Who cites Ivan Stelmakh

200 citations of their works from 196 papers of our library (of the 149,439 whose bibliographies we read) · 60 with a page here, the most cited first · [← Ivan Stelmakh](https://md.research.elamur.ai/free/en/authors/ivan-stelmakh/)

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. **[Active Retrieval Augmented Generation](https://md.research.elamur.ai/free/en/papers/jiang-2023-active-retrieval-augmented-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhengbao Jiang, Frank Xu, Luyu Gao, et al.*2023 · 872 citations*
4. [**Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection arXiv:2310.11511**Akari Asai, Zeqiu Wu, Yizhong Wang, et al.*2023 · 677 citations*](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/)
5. **[Enabling Large Language Models to Generate Text with Citations](https://md.research.elamur.ai/free/en/papers/gao-2023-enabling-large-language-models-generate/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tianyu Gao, Howard Yen, Jiatong Yu, et al.*2023 · 485 citations*
6. [**Fine-Grained Human Feedback Gives Better Rewards for Language Model Training arXiv:2306.01693**Zeqiu Wu, Yushi Hu, Weijia Shi, et al.*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-fine-grained-human-feedback-gives/)
7. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity arXiv:2310.07521**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
8. **[Searching for Best Practices in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/wang-2024-searching-best-practices-retrieval-augmented/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaohua Wang, Zhenghua Wang, Xuan Gao, et al.*2024 · 150 citations*
9. [**Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models arXiv:2212.08037**Bernd Bohnet, Vinh Q. Tran, Pat Verga, et al.*2022 · 138 citations*](https://md.research.elamur.ai/free/en/papers/bohnet-2022-attributed-question-answering-evaluation-modeling/)
10. **[A Critical Evaluation of Evaluations for Long-form Question Answering](https://md.research.elamur.ai/free/en/papers/xu-2023-critical-evaluation-evaluations-long-form/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fangyuan Xu, Yixiao Song, Mohit Iyyer, et al.*2023 · 117 citations*
11. **[ExpertQA: Expert-Curated Questions and Attributed Answers](https://md.research.elamur.ai/free/en/papers/malaviya-2024-expertqa-expert-curated-questions-attributed/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chaitanya Malaviya, Subin Lee, Sihao Chen, et al.*2024 · 113 citations*
12. [**FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets arXiv:2307.10928**Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.*2023 · 100 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-flask-fine-grained-language-model/)
13. [**RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation arXiv:2404.00610**Chi-Min Chan, Chunpu Xu, Ruibin Yuan, et al.*2024 · 90 citations*](https://md.research.elamur.ai/free/en/papers/chan-2024-rq-rag-learning-refine-queries/)
14. [**ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing arXiv:2306.00622**Ryan Liu, Nihar B. Shah*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-reviewergpt-exploratory-study-using/)
15. **[Selectively Answering Ambiguous Questions](https://md.research.elamur.ai/free/en/papers/cole-2023-selectively-answering-ambiguous-questions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jeremy Cole, Michael Zhang, Daniel Gillick, et al.*2023 · 84 citations*
16. **[Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/kim-2023-tree-clarifications-answering-ambiguous-questions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gangwoo Kim, Sungdong Kim, Byeongguk Jeon, et al.*2023 · 84 citations*
17. [**FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research arXiv:2405.13576**Jiajie Jin, Yutao Zhu, Guanting Dong, et al.*2024 · 77 citations*](https://md.research.elamur.ai/free/en/papers/jin-2024-flashrag-modular-toolkit-efficient-retrieval/)
18. **[Conditional Generation with a Question-Answering Blueprint](https://md.research.elamur.ai/free/en/papers/narayan-2023-conditional-generation-question-answering-blueprint/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, et al.*2023 · 70 citations*
19. **[MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/chang-2025-main-rag-multi-agent-filtering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, et al.*2025 · 65 citations*
20. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
21. **[Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-astute-rag-overcoming-imperfect-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fei Wang, Xingchen Wan, Ruoxi Sun, et al.*2025 · 59 citations*
22. [**HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly arXiv:2410.02694**Howard Yen, Tianyu Gao, Minmin Hou, et al.*2024 · 55 citations*](https://md.research.elamur.ai/free/en/papers/yen-2024-helmet-evaluate-long-context-language/)
23. [**TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks arXiv:2310.00752**Dongfu Jiang, Yishan Li, Ge Zhang, et al.*2023 · 50 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-tigerscore-towards-building-explainable-metric/)
24. **[Attribute First, then Generate: Locally-attributable Grounded Text Generation](https://md.research.elamur.ai/free/en/papers/slobodkin-2024-attribute-first-then-generate-locally/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aviv Slobodkin, Eran Hirsch, Arie Cattan, et al.*2024 · 48 citations*
25. **[LUQ: Long-text Uncertainty Quantification for LLMs](https://md.research.elamur.ai/free/en/papers/zhang-2024-luq-long-text-uncertainty-quantification/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Caiqi Zhang, Fangyu Liu, Marco Basaldella, et al.*2024 · 42 citations*
26. [**LLM4SR: A Survey on Large Language Models for Scientific Research arXiv:2501.04306**Ziming Luo, Zonglin Yang, Zexin Xu, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/luo-2025-llm4sr-survey-large-language/)
27. [**AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews arXiv:2408.10365**Keith Tyser, Ben Segev, Gaston Longhitano, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/tyser-2024-ai-driven-review-systems/)
28. [**What Can Natural Language Processing Do for Peer Review? arXiv:2405.06563**Ilia Kuznetsov, Osama Mohammed Afzal, Koen Dercksen, et al.*2024 · 33 citations*](https://md.research.elamur.ai/free/en/papers/kuznetsov-2024-natural-language-processing-peer/)
29. **[Improving Retrieval Augmented Language Model with Self-Reasoning](https://md.research.elamur.ai/free/en/papers/xia-2024-improving-retrieval-augmented-language-model/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yuan Xia, Jingbo Zhou, Zhenhui Shi, et al.*2024 · 32 citations*
30. **[Peer Reviews of Peer Reviews: A Randomized Controlled Trial and Other Experiments](https://md.research.elamur.ai/free/en/papers/goldberg-2023-peer-reviews-peer-reviews/) [PLoS ONE](https://md.research.elamur.ai/free/en/venues/plos-one/)**Alexander Goldberg, Ivan Stelmakh, Kyunghyun Cho, et al.*2023 · 32 citations*
31. **[ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models](https://md.research.elamur.ai/free/en/papers/tan-2024-proxyqa-alternative-framework-evaluating-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haochen Tan, Zhijiang Guo, Zhan Shi, et al.*2024 · 30 citations*
32. **[CLAPnq: Cohesive Long-form Answers from Passages in Natural Questions for RAG systems](https://md.research.elamur.ai/free/en/papers/rosenthal-2025-clapnq-cohesive-long-form-answers/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Sara Rosenthal, Avirup Sil, Radu Florian, et al.*2025 · 29 citations*
33. **[Hierarchical Document Refinement for Long-context Retrieval-augmented Generation](https://md.research.elamur.ai/free/en/papers/jin-2025-hierarchical-document-refinement-long-context/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiajie Jin, Xiaoxi Li, Guanting Dong, et al.*2025 · 25 citations*
34. [**Understanding Retrieval Augmentation for Long-Form Question Answering arXiv:2310.12150**Hung-Ting Chen, Fangyuan Xu, Shane Arora, et al.*2023 · 24 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-understanding-retrieval-augmentation-long-form/)
35. [**Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025 arXiv:2504.09737**Nitya Thakkar, Mert Yuksekgonul, Jake Silberg, et al.*2025 · 21 citations*](https://md.research.elamur.ai/free/en/papers/thakkar-2025-llm-feedback-enhance-review/)
36. **[DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering](https://md.research.elamur.ai/free/en/papers/cheng-2025-dualrag-dual-process-approach-integrate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rong Cheng, Jinyi Liu, Yan Zheng, et al.*2025 · 20 citations*
37. **[RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions](https://md.research.elamur.ai/free/en/papers/liu-2025-rag-instruct-boosting-llms-diverse/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wanlong Liu, Junying Chen, Ke Ji, et al.*2025 · 19 citations*
38. **[RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation](https://md.research.elamur.ai/free/en/papers/dong-2025-rag-critic-leveraging-automated-critic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanting Dong, Jiajie Jin, Xiaoxi Li, et al.*2025 · 14 citations*
39. **[LoGU: Long-form Generation with Uncertainty Expressions](https://md.research.elamur.ai/free/en/papers/yang-2025-logu-long-form-generation-uncertainty/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruihan Yang, Caiqi Zhang, Zhisong Zhang, et al.*2025 · 13 citations*
40. [**Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards arXiv:2505.04966**Jaeho Kim, Yunseok Lee, Seulki Lee*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/kim-2025-position-ai-conference-peer-review/)
41. **[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*
42. **[DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking](https://md.research.elamur.ai/free/en/papers/li-2025-deepsolution-boosting-complex-engineering-solution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhuoqun Li, Haiyang Yu, Xuanang Chen, et al.*2025 · 10 citations*
43. [**Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment arXiv:2411.03417**Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/goldberg-2024-usefulness-llms-author-checklist-assistant/)
44. **[DebateQA: Evaluating Question Answering on Debatable Knowledge](https://md.research.elamur.ai/free/en/papers/xu-2026-debateqa-evaluating-question-answering-debatable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rongwu Xu, Xuan Qi, Zehan Qi, et al.*2026 · 7 citations*
45. [**PreWoMe: Exploiting Presuppositions as Working Memory for Long Form Question Answering**Wookje Han, Jinsol Park, Kyungjae Lee*2023 · 4 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-prewome-exploiting-presuppositions-as-working/)
46. **[Explaining Generalization of AI-Generated Text Detectors Through Linguistic Analysis](https://md.research.elamur.ai/free/en/papers/xia-2026-explaining-generalization-ai-generated-text/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yuxi Xia, Kinga Stańczak, Benjamin Roth*2026 · 2 citations*
47. **[QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/min-2026-quco-rag-quantifying-uncertainty-pre/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dehai Min, Kailin Zhang, Tongtong Wu, et al.*2026 · 2 citations*
48. **[Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/wang-2026-reinforced-informativeness-optimization-long-form/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuhao Wang, Ruiyang Ren, Yucheng Wang, et al.*2026 · 2 citations*
49. **[ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling](https://md.research.elamur.ai/free/en/papers/xu-2026-thinknote-enhancing-knowledge-integration-utilization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhipeng Xu, Zhenghao Liu, Yukun Yan, et al.*2026 · 2 citations*
50. **[Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models](https://md.research.elamur.ai/free/en/papers/schreieder-2026-attribution-citation-quotation-survey-evidence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tobias Schreieder, Tim Schopf, Michael Färber*2026 · 1 citation*
51. **[RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems](https://md.research.elamur.ai/free/en/papers/liu-2026-rate-reviewer-profiling-annotation-free/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weicong Liu, Zixuan Yang, Yibo Zhao, et al.*2026 · 1 citation*
52. **[On Reference (In-)Determinacy in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/chen-2025-reference-determinacy-natural-language-inference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sihao Chen, Chaitanya Malaviya, Alex Fabrikant, et al.*2025 · 1 citation*
53. **[KnowledgeBerg: Evaluating Systematic Knowledge Coverage and Compositional Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-knowledgeberg-evaluating-systematic-knowledge-coverage/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiao Zhang, Qianru Meng, Yongjian Chen, et al.*2026*
54. **[LCR-RAG: Enhancing Logical Consistency in Retrieval-Augmented Generation via Neuro-symbolic Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/zheng-2026-lcr-rag-enhancing-logical-consistency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenxiang Zheng, Guo Tang, Shixin Jiang, et al.*2026*
55. **[MARCH: Evaluating the Intersection of Ambiguity Interpretation and Multi-hop Inference](https://md.research.elamur.ai/free/en/papers/park-2026-march-evaluating-intersection-ambiguity-interpretation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jeonghyun Park, Ingeol Baek, Seunghyun Yoon, et al.*2026*
56. **[MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/xiao-2026-mass-rag-multi-agent-synthesis/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xingchen Xiao, Heyan Huang, Runheng Liu, et al.*2026*
57. **[Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling](https://md.research.elamur.ai/free/en/papers/liu-2026-mitigating-judgment-preference-bias-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shuliang Liu, Zhipeng Xu, Zhenghao Liu, et al.*2026*
58. **[Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem](https://md.research.elamur.ai/free/en/papers/song-2026-rethinking-retrieval-augmented-generation-as/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lichang Song, Ting Long, Yi Chang*2026*
59. **[The Mark Fades: Adaptive Evolutionary Paraphrase-based Attack against LLM Watermarks](https://md.research.elamur.ai/free/en/papers/zhao-2026-mark-fades-adaptive-evolutionary-paraphrase/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yusheng Zhao, Jian Zhao, Tianle Zhang, et al.*2026*
60. **[Truth or Dare: Analyzing LLM Susceptibility to External Evidence of Varying Factuality](https://md.research.elamur.ai/free/en/papers/su-2026-truth-or-dare-analyzing-llm/) [Workshop on Trustworthy NLP](https://md.research.elamur.ai/free/en/venues/trustnlp/)**Han-Yu Su, Kuan-Yu Chu, Yung-Hui Li, et al.*2026*

136 more citing papers of our library have no page here yet.

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