---
title: "Who cites Ruochen Xu — page 4 — 1,614 papers of our library"
description: "1,614 papers of the Elamur Research library cite the works of Ruochen Xu, found in their bibliographies · page 4 of 4 · 301–329; the most cited first."
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---
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# Who cites Ruochen Xu

1,630 citations of their works from 1,614 papers of our library (of the 149,439 whose bibliographies we read) · 329 with a page here, the most cited first · page 4 of 4 · 301–329 · [← Ruochen Xu](https://md.research.elamur.ai/free/en/authors/ruochen-xu/)

[← prev](https://md.research.elamur.ai/free/en/authors/ruochen-xu/cited-by/page/3/) [1](https://md.research.elamur.ai/free/en/authors/ruochen-xu/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/ruochen-xu/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/authors/ruochen-xu/cited-by/page/3/) **4**

301. **[Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering](https://md.research.elamur.ai/free/en/papers/wang-2026-putting-captions-test-evaluating-video/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zizhen Wang, Bo Feng, Zhengfeng Lai, et al.*2026*
302. **[QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/qu-2026-quantumqa-enhancing-scientific-reasoning-physics/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Songxin Qu, Tai-Ping Sun, Yun-Jie Wang, et al.*2026*
303. **[R ESEARCH QA: Evaluating Scholarly Question Answering at Scale Across 75 Fields with Survey-Mined Questions and Rubrics](https://md.research.elamur.ai/free/en/papers/yifei-2026-r-esearch-qa-evaluating-scholarly/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Li S. Yifei, Allen Chang, Chaitanya Malaviya, et al.*2026*
304. **[RealTalk-CN: A Realistic Chinese Speech Task-Oriented Dialogue Benchmark with Cross-Modal Analysis](https://md.research.elamur.ai/free/en/papers/wang-2026-realtalk-cn-realistic-chinese-speech/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Enzhi Wang, Jiaming Zhou, Yuhang Jia, et al.*2026*
305. [**Recent Advances in Text-attributed Graph Learning via Large Models (大模型驱动的文本属性图学习研究进展) Acta Automatica Sinica (自动化学报)**Yanping Zheng (郑艳萍), Jiarui Ji (季嘉蕊), Yuwei Hu (胡雨韦), et al.*2026*](https://md.research.elamur.ai/free/en/papers/zheng-2026-recent-advances-text/)
306. **[ReSQL: Self-Improving Framework for Reasoning-Aware Text-to-SQL Dataset Generation](https://md.research.elamur.ai/free/en/papers/park-2026-resql-self-improving-framework-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Minjun Park, Yongju Seong, Myoseop Sim, et al.*2026*
307. **[Reward Modeling for Scientific Writing Evaluation](https://md.research.elamur.ai/free/en/papers/sahinuc-2026-reward-modeling-scientific-writing-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Furkan Şahinuç, Subhabrata Dutta, Iryna Gurevych*2026*
308. **[RiT: Rubrics-in-Thinking Reinforcement Learning for Improved Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/tian-2026-rit-rubrics-thinking-reinforcement-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaobin Tian, Shuai Yuan, Muyun Ding, et al.*2026*
309. **[SAD: A Large-Scale Strategic Argumentative Dialogue Dataset](https://md.research.elamur.ai/free/en/papers/liu-2026-sad-large-scale-strategic-argumentative/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**YongKang Liu, Jiayang Yu, Mingyang Wang, et al.*2026*
310. **[SAUCE: Summary Analysis Using Conversation Entailment](https://md.research.elamur.ai/free/en/papers/sung-2026-sauce-summary-analysis-conversation-entailment/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Man-Ling Sung, Hemanth Kandula, Jeff Ma, et al.*2026*
311. **[SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization](https://md.research.elamur.ai/free/en/papers/wang-2026-scurank-ranking-multiple-candidate-summaries/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bo-Jyun Wang, Ying-Jia Lin, Hung-Yu Kao*2026*
312. **[StealthGraph: Exposing Domain-Specific Risks in LLMs through Knowledge-Graph-Guided Harmful Prompt Generation](https://md.research.elamur.ai/free/en/papers/zheng-2026-stealthgraph-exposing-domain-specific-risks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huawei Zheng, Xinqi Jiang, Sen Yang, et al.*2026*
313. **[Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej](https://md.research.elamur.ai/free/en/papers/nigam-2026-structured-legal-document-generation-india/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Shubham Kumar Nigam, Deepak Patnaik Balaramamahanthi, Noel Shallum, et al.*2026*
314. **[Tailored Primitive Initialization is the Secret Key to Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yao-2026-tailored-primitive-initialization-is-secret/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yihang Yao, Guangtao Zeng, Raina Wu, et al.*2026*
315. **[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*
316. **[TeachMaster: Generative Teaching via Code](https://md.research.elamur.ai/free/en/papers/wang-2026-teachmaster-generative-teaching-code/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuheng Wang, Runde Yang, Lin Wu, et al.*2026*
317. **[TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training](https://md.research.elamur.ai/free/en/papers/wang-2026-tikmix-efficient-semi-dynamic-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifan Wang, Binbinliu, Fengze Liu, et al.*2026*
318. **[ToMELP: A Theory-of-Mind Benchmark for Route-Controlled Persuasion under the Elaboration Likelihood Model](https://md.research.elamur.ai/free/en/papers/wang-2026-tomelp-theory-mind-benchmark-route/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ruirui Wang, Haoran Zhang, Tian Lan, et al.*2026*
319. **[Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation](https://md.research.elamur.ai/free/en/papers/ma-2026-too-open-opinion-embracing-open/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Bolei Ma, Yong Cao, Indira Sen, et al.*2026*
320. **[Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice](https://md.research.elamur.ai/free/en/papers/wang-2026-ip-intelligence-benchmarking-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qiyao Wang, Guhong Chen, Hongbo Wang, et al.*2026*
321. **[TruthTrap: A Bilingual Benchmark for Evaluating Factually Correct Yet Misleading Information in Question Answering](https://md.research.elamur.ai/free/en/papers/shafiei-2026-truthtrap-bilingual-benchmark-evaluating-factually/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mohammadamin Shafiei, Hamidreza Saffari, Mohammad Taher Pilehvar, et al.*2026*
322. **[UniCM: A Unified Consistency Model For Efficient Multimodal Generation and Understanding](https://md.research.elamur.ai/free/en/papers/xu-2026-unicm-unified-consistency-model-efficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenkai Xu, Xu Wang, Zhenyi Liao, et al.*2026*
323. **[UniDataBench: Evaluating Data Analytics Agents Across Structured and Unstructured Data](https://md.research.elamur.ai/free/en/papers/weng-2026-unidatabench-evaluating-data-analytics-agents/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Han Weng, Zhou Liu, Yuanfeng Song, et al.*2026*
324. **[Verifying the Subjective: Structured Multilingual Rewards for Low-Resource Alignment](https://md.research.elamur.ai/free/en/papers/sha-2026-verifying-subjective-structured-multilingual-rewards/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiu Sha, Mengxiao Zhu*2026*
325. **[What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance](https://md.research.elamur.ai/free/en/papers/watson-2026-what-makes-good-query-measuring/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**William Watson, Nicole Cho, Sumitra Ganesh, et al.*2026*
326. **[When Users Are Happy but Agents Are Wrong: Multi-Dimensional Evaluation of Tool-Augmented Dialogue](https://md.research.elamur.ai/free/en/papers/shourya-2026-when-users-are-happy-but/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Tanya Shourya, Yingfan Wang, Zhaoyi Joey Hou, et al.*2026*
327. [**Survey of Tool Use in Large Language Models (大语言模型的工具使用综述) Acta Automatica Sinica (自动化学报)**Yining Zheng (郑逸宁), Zhen Yu (余镇), Bufan Li (李不凡), et al.*2025*](https://md.research.elamur.ai/free/en/papers/zheng-2025-tool-use-large/)
328. **[Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)](https://md.research.elamur.ai/free/en/papers/liu-2024-hallucinations-large-language/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Zeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.*2024*
329. **[Survey on Commonsense Question Answering (常识问答研究综述)](https://md.research.elamur.ai/free/en/papers/fan-2023-commonsense-question-answering/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yifan Fan (范怡帆), Bowei Zou (邹博伟), Qingting Xu (徐庆婷), et al.*2023*

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1,285 more citing papers of our library have no page here yet.

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