---
title: "Who cites Ankit Singh Rawat — 174 papers of our library"
description: "174 papers of the Elamur Research library cite the works of Ankit Singh Rawat, found in their bibliographies; the most cited first."
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---
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# Who cites Ankit Singh Rawat

174 citations of their works from 174 papers of our library (of the 149,439 whose bibliographies we read) · 42 with a page here, the most cited first · [← Ankit Singh Rawat](https://md.research.elamur.ai/free/en/authors/ankit-singh-rawat/)

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. [**RoFormer: Enhanced Transformer with Rotary Position Embedding arXiv:2104.09864**Jianlin Su, Yu Lu, Shengfeng Pan, et al.*2021 · 3,028 citations*](https://md.research.elamur.ai/free/en/papers/su-2021-roformer-enhanced-transformer-rotary-position/)
3. **[Big Bird: Transformers for Longer Sequences](https://md.research.elamur.ai/free/en/papers/zaheer-2020-big-bird-transformers-longer-sequences/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Manzil Zaheer, Guru Guruganesh, Avinava Dubey, et al.*2020 · 1,023 citations*
4. **[A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Wenqi Fan; Yujuan Ding; Liangbo Ning; Shijie Wang; Hengyun Li; Dawei Yin; Tat-Seng Chua;, Qing Li*2024 · 1,010 citations*
5. **[Benchmarking Large Language Models in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/chen-2023-benchmarking-large-language-models-retrieval/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Jiawei Chen, Hongyu Lin, Xianpei Han, et al.*2023 · 629 citations*
6. [**Large Language Models Can Be Easily Distracted by Irrelevant Context arXiv:2302.00093**Freda Shi, Xinyun Chen, Kanishka Misra, et al.*2023 · 534 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/)
7. [**Hallucination is Inevitable: An Innate Limitation of Large Language Models arXiv:2401.11817**Ziwei Xu, Sanjay Jain, Mohan Kankanhalli*2024 · 312 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-hallucination-inevitable-innate-limitation-large/)
8. [**What learning algorithm is in-context learning? Investigations with linear models arXiv:2211.15661**Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2022-learning-algorithm-context-learning-investigations/)
9. **[What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://md.research.elamur.ai/free/en/papers/garg-2022-transformers-learn-context-case-study/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shivam Garg, Dimitris Tsipras, Percy Liang, et al.*2022 · 209 citations*
10. [**Making Retrieval-Augmented Language Models Robust to Irrelevant Context arXiv:2310.01558**Ori Yoran, Tomer Wolfson, Ori Ram, et al.*2023 · 195 citations*](https://md.research.elamur.ai/free/en/papers/yoran-2023-making-retrieval-augmented-language-models/)
11. [**RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models arXiv:2310.00746**Zekun Moore Wang, Zhongyuan Peng, Haoran Que, et al.*2023 · 187 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-rolellm-benchmarking-eliciting-enhancing-role/)
12. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations*
13. [**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/)
14. **[Context-faithful Prompting for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2023-context-faithful-prompting-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenxuan Zhou, Sheng Zhang, Hoifung Poon, et al.*2023 · 148 citations*
15. **[On the Ability and Limitations of Transformers to Recognize Formal Languages](https://md.research.elamur.ai/free/en/papers/bhattamishra-2020-ability-limitations-transformers-recognize-formal/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Satwik Bhattamishra, Kabir Ahuja, Navin Goyal*2020 · 113 citations*
16. **[R-Tuning: Instructing Large Language Models to Say ‘I Don't Know'](https://md.research.elamur.ai/free/en/papers/zhang-2024-r-tuning-instructing-large-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hanning Zhang, Shizhe Diao, Yong Lin, et al.*2024 · 99 citations*
17. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
18. **[DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering](https://md.research.elamur.ai/free/en/papers/neeman-2023-disentqa-disentangling-parametric-contextual-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ella Neeman, Roee Aharoni, Or Honovich, et al.*2023 · 85 citations*
19. **[HalluLens: LLM Hallucination Benchmark](https://md.research.elamur.ai/free/en/papers/bang-2025-hallulens-llm-hallucination-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yejin Bang, Ziwei Ji, Alan Schelten, et al.*2025 · 82 citations*
20. **[Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training](https://md.research.elamur.ai/free/en/papers/fang-2024-enhancing-noise-robustness-retrieval-augmented/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Feiteng Fang, Yuelin Bai, Shiwen Ni, et al.*2024 · 75 citations*
21. **[Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity](https://md.research.elamur.ai/free/en/papers/hao-2022-formal-language-recognition-hard-attention/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Yiding Hao, Dana Angluin, Robert Frank*2022 · 62 citations*
22. [**Teaching Arithmetic to Small Transformers arXiv:2307.03381**Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-teaching-arithmetic-small-transformers/)
23. [**Resolving Knowledge Conflicts in Large Language Models arXiv:2310.00935**Yike Wang, Shangbin Feng, Heng Wang, et al.*2023 · 48 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-resolving-knowledge-conflicts-large-language/)
24. **[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*
25. **[FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/zhang-2025-faithfulrag-fact-level-conflict-modeling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qinggang Zhang, Zhishang Xiang, Yilin Xiao, et al.*2025 · 27 citations*
26. **[The Distracting Effect: Understanding Irrelevant Passages in RAG](https://md.research.elamur.ai/free/en/papers/amiraz-2025-distracting-effect-understanding-irrelevant-passages/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chen Amiraz, Florin Cuconasu, Simone Filice, et al.*2025 · 21 citations*
27. **[Understanding the Dark Side of LLMs' Intrinsic Self-Correction](https://md.research.elamur.ai/free/en/papers/zhang-2025-understanding-dark-side-llms-intrinsic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingjie Zhang, Di Wang, Haoting Qian, et al.*2025 · 21 citations*
28. [**Source-Aware Training Enables Knowledge Attribution in Language Models arXiv:2404.01019**Muhammad Khalifa, David Wadden, Emma Strubell, et al.*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/khalifa-2024-source-aware-training-enables-knowledge/)
29. **[Improving Multilingual Retrieval-Augmented Language Models through Dialectic Reasoning Argumentations](https://md.research.elamur.ai/free/en/papers/ranaldi-2025-improving-multilingual-retrieval-augmented-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Leonardo Ranaldi, Federico Ranaldi, Fabio Massimo Zanzotto, et al.*2025 · 12 citations*
30. **[Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning](https://md.research.elamur.ai/free/en/papers/xu-2025-parenting-optimizing-knowledge-selection-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yongxin Xu, Ruizhe Zhang, Xinke Jiang, et al.*2025 · 9 citations*
31. [**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/)
32. **[A Review of Incorporating Psychological Theories in LLMs](https://md.research.elamur.ai/free/en/papers/liu-2026-review-incorporating-psychological-theories-llms/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zizhou Liu, Ziwei Gong, Lin Ai, et al.*2026 · 1 citation*
33. **[Knowing When Not to Answer: Lightweight KB-Aligned OOD Detection for Safe RAG](https://md.research.elamur.ai/free/en/papers/triantafyllopoulos-2026-knowing-when-not-answer-lightweight/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Triantafyllopoulos, Renyi Qu, Salvatore Giorgi, et al.*2026 · 1 citation*
34. **[Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling](https://md.research.elamur.ai/free/en/papers/rodkin-2026-beyond-memorization-extending-reasoning-depth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ivan Rodkin, Daniil Orel, Konstantin Smirnov, et al.*2026*
35. **[CoDA: Restoring Contextual Dominance via Copy-Encouraged Attention Intervention for Mitigating RAG Hallucinations](https://md.research.elamur.ai/free/en/papers/shi-2026-coda-restoring-contextual-dominance-copy/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**JinWei Shi, Qizhuo Xie, Qianzi Hou, et al.*2026*
36. **[DKME: Rethinking Coupled Knowledge Memory for Lifelong Model Editing of Large Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2026-dkme-rethinking-coupled-knowledge-memory/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Guanyu Zheng, Wang Zhenyu, He Tingting, et al.*2026*
37. **[Do Transformers Grok Succinct Algorithms? Mechanistic Evidence for Counting Circuits](https://md.research.elamur.ai/free/en/papers/zhao-2026-do-transformers-grok-succinct-algorithms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yizheng Zhao*2026*
38. **[GSM-Noise: Exploring and Enhancing Large Language Models' Reasoning under Noisy Inputs](https://md.research.elamur.ai/free/en/papers/zhang-2026-gsm-noise-exploring-enhancing-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhengxin Zhang, Chengyu Huang, Xufu Liu, et al.*2026*
39. **[Language Acquisition Device in Large Language Models](https://md.research.elamur.ai/free/en/papers/mita-2026-language-acquisition-device-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Masato Mita, Taiga Someya, Ryo Yoshida, et al.*2026*
40. **[LOKA: Conflict-Aware LLM Knowledge Update with Adaptive Knowledge Memory](https://md.research.elamur.ai/free/en/papers/zhang-2026-loka-conflict-aware-llm-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Binchi Zhang, Zhengzhang Chen, Zaiyi Zheng, et al.*2026*
41. **[SCOPE: Boosting LLM Efficiency with Scoped Position Encoding](https://md.research.elamur.ai/free/en/papers/qi-2026-scope-boosting-llm-efficiency-scoped/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingguo Qi, Hongyang Chen, Zhao Li*2026*
42. **[Understanding the Prompt Sensitivity](https://md.research.elamur.ai/free/en/papers/liu-2026-understanding-prompt-sensitivity/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Liu, Chenhui Chu*2026*

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

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