---
title: "Who cites Vincent Perot — 238 papers of our library"
description: "238 papers of the Elamur Research library cite the works of Vincent Perot, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/vincent-perot/cited-by/"
---
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# Who cites Vincent Perot

238 citations of their works from 238 papers of our library (of the 149,439 whose bibliographies we read) · 54 with a page here, the most cited first · [← Vincent Perot](https://md.research.elamur.ai/free/en/authors/vincent-perot/)

1. [**Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities arXiv:2507.06261**Gheorghe Comanici, Eric Bieber, Mike Schaekermann, et al.*2025 · 974 citations*](https://md.research.elamur.ai/free/en/papers/comanici-2025-gemini-2-5-pushing-frontier/)
2. **[Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.*2022 · 754 citations*
3. [**RULER: What's the Real Context Size of Your Long-Context Language Models? arXiv:2404.06654**Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, et al.*2024 · 400 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2024-ruler-s-real-context-size/)
4. [**A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications arXiv:2402.07927**Pranab Sahoo; Ayush Kumar Singh; Sriparna Saha; Vinija Jain; Samrat Mondal;, Aman Chadha*2024 · 375 citations*](https://md.research.elamur.ai/free/en/papers/sahoo-2024-systematic-survey-prompt-engineering-large/)
5. **[Large Language Models for Data Annotation and Synthesis: A Survey](https://md.research.elamur.ai/free/en/papers/tan-2024-large-language-models-data-annotation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Tan, Dawei Li, Song Wang, et al.*2024 · 262 citations*
6. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
7. [**Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step arXiv:2402.16906**Li Zhong, Zilong Wang, Jingbo Shang*2024 · 124 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-debug-like-human-large-language/)
8. **[ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/asai-2022-attempt-parameter-efficient-multi-task/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Akari Asai, Mohammadreza Salehi, Matthew Peters, et al.*2022 · 122 citations*
9. [**World Model on Million-Length Video And Language With Blockwise RingAttention arXiv:2402.08268**Hao Liu, Wilson Yan, Matei Zaharia, et al.*2024 · 99 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-world-model-million-length-video/)
10. [**AFlow: Automating Agentic Workflow Generation arXiv:2410.10762**Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-aflow-automating-agentic-workflow/)
11. [**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/)
12. [**Exploring the Benefits of Training Expert Language Models over Instruction Tuning arXiv:2302.03202**Joel Jang, Seungone Kim, Seonghyeon Ye, et al.*2023 · 63 citations*](https://md.research.elamur.ai/free/en/papers/jang-2023-exploring-benefits-training-expert-language/)
13. [**Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries arXiv:2409.12640**Kiran Vodrahalli; Santiago Ontanon; Nilesh Tripuraneni; Kelvin Xu; Sanil Jain; Rakesh Shivanna; Jeffrey Hui; Nishanth Dikkala; Mehran Kazemi; Bahare Fatemi; Rohan Anil; Ethan Dyer; Siamak Shakeri; Roopali Vij; Harsh Mehta; Vinay Ramasesh; Quoc Le; Ed Chi; Yifeng Lu; Orhan Firat; Angeliki Lazaridou; Jean-Baptiste Lespiau; Nithya Attaluri;, Kate Olszewska*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/vodrahalli-2024-michelangelo-long-context-evaluations-haystacks/)
14. **[S²R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/ma-2025-s-2-r-teaching-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruotian Ma, Peisong Wang, Cheng Liu, et al.*2025 · 26 citations*
15. **[Table-Critic: A Multi-Agent Framework for Collaborative Criticism and Refinement in Table Reasoning](https://md.research.elamur.ai/free/en/papers/yu-2025-table-critic-multi-agent-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peiying Yu, Guoxin Chen, Jingjing Wang*2025 · 25 citations*
16. [**Baichuan-M1: Pushing the Medical Capability of Large Language Models arXiv:2502.12671**Bingning Wang, Haizhou Zhao, Huozhi Zhou, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-baichuan-m1-pushing-medical/)
17. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations*
18. [**GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity? arXiv:2502.05252**Yang Zhou, Hongyi Liu, Zhuoming Chen, et al.*2025 · 21 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2025-gsm-infinite-llms-behave-over/)
19. **[Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs](https://md.research.elamur.ai/free/en/papers/xia-2025-beyond-chain-thought-survey-chain/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Yu Xia, Rui Wang, Xu Liu, et al.*2025 · 19 citations*
20. **[Learning from Diverse Reasoning Paths with Routing and Collaboration](https://md.research.elamur.ai/free/en/papers/lei-2025-learning-diverse-reasoning-paths-routing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhenyu Lei, Zhen Tan, Song Wang, et al.*2025 · 11 citations*
21. **[RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals](https://md.research.elamur.ai/free/en/papers/zhang-2025-rot-enhancing-table-reasoning-iterative/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xuanliang Zhang, Dingzirui Wang, Keyan Xu, et al.*2025 · 10 citations*
22. [**100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models arXiv:2505.00551**Chong Zhang, Yue Deng, Xiang Lin, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-100-days-after-deepseek/)
23. **[On Many-Shot In-Context Learning for Long-Context Evaluation](https://md.research.elamur.ai/free/en/papers/zou-2025-many-shot-context-learning-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kaijian Zou, Muhammad Khalifa, Lu Wang*2025 · 8 citations*
24. **[TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models](https://md.research.elamur.ai/free/en/papers/he-2025-tablelora-low-rank-adaptation-table/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyi He, Yihao Liu, Mengyu Zhou, et al.*2025 · 8 citations*
25. **[MetaScale: Test-Time Scaling with Evolving Meta-Thoughts](https://md.research.elamur.ai/free/en/papers/liu-2026-metascale-test-time-scaling-evolving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qin Liu, Wenxuan Zhou, Nan Xu, et al.*2026 · 7 citations*
26. **[TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering](https://md.research.elamur.ai/free/en/papers/zhu-2025-tableeval-real-world-benchmark-complex/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Junnan Zhu, Jingyi Wang, Bohan Yu, et al.*2025 · 7 citations*
27. **[Weaver: Interweaving SQL and LLM for Table Reasoning](https://md.research.elamur.ai/free/en/papers/khoja-2025-weaver-interweaving-sql-llm-table/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rohit Khoja, Devanshu Gupta, Yanjie Fu, et al.*2025 · 6 citations*
28. **[When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables](https://md.research.elamur.ai/free/en/papers/ye-2026-when-tableqa-meets-noise-dual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shenghao Ye, Yu Guo, Dong Jin, et al.*2026 · 4 citations*
29. **[CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability](https://md.research.elamur.ai/free/en/papers/peng-2025-cafe-retrieval-head-based-coarse/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Han Peng, Jinhao Jiang, Zican Dong, et al.*2025 · 4 citations*
30. **[ClaimDB: A Fact Verification Benchmark over Large Structured Data](https://md.research.elamur.ai/free/en/papers/theologitis-2026-claimdb-fact-verification-benchmark-over/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michael Theologitis, Preetam Prabhu Srikar Dammu, Chirag Shah, et al.*2026 · 2 citations*
31. **[Exploring Generative Process Reward Modeling for Semi-Structured Data: A Case Study of Table Question Answering](https://md.research.elamur.ai/free/en/papers/tang-2026-exploring-generative-process-reward-modeling/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Lei Tang, Wei Zhou, Mohsen Mesgar*2026 · 2 citations*
32. **[Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation](https://md.research.elamur.ai/free/en/papers/zhou-2026-table-question-answering-era-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wei Zhou, Bolei Ma, Annemarie Friedrich, et al.*2026 · 2 citations*
33. [**AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data arXiv:2507.18442**Rana Alshaikh, Israa Alghanmi, Shelan Jeawak*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/alshaikh-2025-aratable-benchmarking-llms-reasoning-understanding/)
34. **[CoReTab: Improving Multimodal Table Understanding with Code-driven Reasoning](https://md.research.elamur.ai/free/en/papers/nguyen-2026-coretab-improving-multimodal-table-understanding/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Van-Quang Nguyen, Takayuki Okatani*2026 · 1 citation*
35. **[Efficient Table Retrieval and Understanding with Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-efficient-table-retrieval-understanding-multimodal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuoyan Xu, Haoyang Fang, Boran Han, et al.*2026 · 1 citation*
36. **[Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual Learning](https://md.research.elamur.ai/free/en/papers/wu-2026-agent-dice-disentangling-knowledge-updates/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zheng Wu, Xingyu Lou, Xinbei Ma, et al.*2026*
37. **[Asking language models how to represent data for fine-tuning](https://md.research.elamur.ai/free/en/papers/singh-2026-asking-language-models-how-represent/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Usneek Singh, Ananya Singha, Abhijeet Awasthi, et al.*2026*
38. **[Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA](https://md.research.elamur.ai/free/en/papers/maheshwary-2026-augmenting-llm-reasoning-dynamic-notes/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Rishabh Maheshwary, Masoud Hashemi, Khyati Mahajan, et al.*2026*
39. **[Balancing Knowledge Breadth and Task Depth for Effective Domain Adaptation Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhang-2026-balancing-knowledge-breadth-task-depth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mu Zhang, Yuxiang Chu, Guangya Yu, et al.*2026*
40. **[Can LLMs Self-Correct Table Reasoning Errors?](https://md.research.elamur.ai/free/en/papers/shaikh-2026-can-llms-self-correct-table/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Farseen Shaikh*2026*
41. **[Exploring Fine-Tuning for In-Context Retrieval and Efficient KV-Caching in Long-Context Language Models](https://md.research.elamur.ai/free/en/papers/molfese-2026-exploring-fine-tuning-context-retrieval/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Francesco Maria Molfese, Momchil Hardalov, Rexhina Blloshmi, et al.*2026*
42. **[Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding](https://md.research.elamur.ai/free/en/papers/zhou-2026-mixture-minds-multi-agent-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuhang Zhou, Mingrui Zhang, Ke Li, et al.*2026*
43. **[MPBoCo: Multimodal Prompt-based Boundary-enhanced Continual Framework for Joint Entity and Relation Extraction](https://md.research.elamur.ai/free/en/papers/sun-2026-mpboco-multimodal-prompt-based-boundary/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanglu Sun, Xinyu Liu, Lili Liang, et al.*2026*
44. **[Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection](https://md.research.elamur.ai/free/en/papers/niu-2026-routing-generated-data-annotation-free/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyi Niu, Justin Chen, Genta Indra Winata, et al.*2026*
45. **[SLoRA: Balancing Plasticity and Forgetting in Large Language Models for Continual Learning](https://md.research.elamur.ai/free/en/papers/yang-2026-slora-balancing-plasticity-forgetting-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lina Yang, Yusheng Liao, Yanfeng Wang, et al.*2026*
46. **[SMART: Semantic Header Flattening and Pseudo-Code-Style Reasoning for LLM-based Complex Table Question Answering](https://md.research.elamur.ai/free/en/papers/zhang-2026-smart-semantic-header-flattening-pseudo/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yongshuo Zhang, Jixiong Chen, Kaihe xu, et al.*2026*
47. **[Soft Orthogonal Low-Rank Adaptation for Knowledge Sharing in Large Language Model Continual Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-soft-orthogonal-low-rank-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yitong Wang, Xue Han, WenChun Gao, et al.*2026*
48. **[Table-R1: Region-based Reinforcement Learning for Table Understanding](https://md.research.elamur.ai/free/en/papers/wu-2026-table-r1-region-based-reinforcement/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhenhe Wu, Jian Yang, Zhongjiang He, et al.*2026*
49. **[Think Like You Execute: Verifiable Chain of Thought from Program Traces](https://md.research.elamur.ai/free/en/papers/thakur-2026-think-like-you-execute-verifiable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shailja Thakur, Vaibhav Saxena, Rohan Kulkarni, et al.*2026*
50. **[Towards Robust Real-World Spreadsheet Understanding with Multi-Agent Multi-Format Reasoning](https://md.research.elamur.ai/free/en/papers/ren-2026-robust-real-world-spreadsheet-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Houxing Ren, Mingjie Zhan, Zimu Lu, et al.*2026*
51. **[TrustTable: A Neuro-Symbolic Auditing Framework for Faithful Table QA](https://md.research.elamur.ai/free/en/papers/zhao-2026-trusttable-neuro-symbolic-auditing-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guangzhen Zhao, Dechang Kong, Tongyu Wu, et al.*2026*
52. **[Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing](https://md.research.elamur.ai/free/en/papers/yuan-2026-verify-before-you-commit-faithful/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenhao Yuan, Chenchen Lin, Jian Chen, et al.*2026*
53. **[What Question Did You Answer? Refining Contact Center Evaluation Plans via Backward Questions](https://md.research.elamur.ai/free/en/papers/sood-2026-what-question-did-you-answer/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Prajwal Sood, Rushikesh Pawar, Digvijay Anil Ingle, et al.*2026*
54. **[LC-Eval: A Bilingual Multi-Task Evaluation Benchmark for Long-Context Understanding](https://md.research.elamur.ai/free/en/papers/jubair-2025-lc-eval-bilingual-multi-task/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sheikh Jubair, Arwa Omayrah, Amal Alshammari, et al.*2025*

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

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