🧑‍🔬 Who cites Xuansheng Wu

115 citations of their works from 115 papers of our library (of the 149,439 whose bibliographies we read) · 35 with a page here, the most cited first · ← Xuansheng Wu

1. WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the WildarXiv:2406.04770Bill Yuchen Lin, Yuntian Deng, Khyathi Chandu, et al.2024 · 105 citations
2. Generalizing Verifiable Instruction FollowingConference on Neural Information Processing Systems (NeurIPS)Valentina Pyatkin, Saumya Malik, Victoria Graf, et al.2025 · 59 citations
3. Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language ModelsarXiv:2404.02823Haoran Sun, Lixin Liu, Junjie Li, et al.2024 · 40 citations
4. Checklists Are Better Than Reward Models For Aligning Language ModelsConference on Neural Information Processing Systems (NeurIPS)Vijay Viswanathan, Yanchao Sun, Shuang Ma, et al.2025 · 37 citations
5. Direct Judgement Preference OptimizationConference on Empirical Methods in Natural Language Processing (EMNLP)PeiFeng Wang, Austin Xu, Yilun Zhou, et al.2025 · 35 citations
6. CFBench: A Comprehensive Constraints-Following Benchmark for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Tao Zhang, ChengLIn Zhu, Yanjun Shen, et al.2025 · 30 citations
7. OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM AlignmentAnnual Meeting of the Association for Computational Linguistics (ACL)Tianci Liu, Ran Xu, Tony Yu, et al.2026 · 25 citations
8. IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Xinghua Zhang, Haiyang Yu, Cheng Fu, et al.2025 · 14 citations
9. Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Tingchen Fu, Yafu Li, Jiawei Gu, et al.2026 · 12 citations
10. LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context ScenariosAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaodong Wu, Minhao Wang, Yichen Liu, et al.2025 · 11 citations
11. AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction FollowingAnnual Meeting of the Association for Computational Linguistics (ACL)Yun He, Wenzhe Li, Hejia Zhang, et al.2026 · 9 citations
12. UltraIF: Advancing Instruction Following from the WildConference on Empirical Methods in Natural Language Processing (EMNLP)Kaikai An, Li Sheng, Ganqu Cui, et al.2025 · 7 citations
13. What Prompts Don't Say: Understanding and Managing Underspecification in LLM PromptsFindings of the Association for Computational LinguisticsChenyang Yang, Yike Shi, Qianou Ma, et al.2026 · 6 citations
14. EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Tao Zou, Xinghua Zhang, Haiyang Yu, et al.2025 · 5 citations
15. MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language ModelsFindings of the Association for Computational LinguisticsJunjie Ye, Caishuang Huang, Zhuohan Chen, et al.2026 · 4 citations
16. OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic CodingAnnual Meeting of the Association for Computational Linguistics (ACL)Deming Ding, Shichun Liu, Enhui Yang, et al.2026 · 4 citations
17. Spotlight Your Instructions: Instruction-following with Dynamic Attention SteeringConference of the European Chapter of the Association for Computational Linguistics (EACL)Praveen Venkateswaran, Danish Contractor2026 · 4 citations
18. MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive TrainingAnnual Meeting of the Association for Computational Linguistics (ACL)Hui Huang, Jiaheng Liu, Yancheng He, et al.2025 · 4 citations
19. KCIF: Knowledge-Conditioned Instruction FollowingLanguage Resources and Evaluation Conference (LREC)Rudra Murthy, Praveen Venkateswaran, Prince Kumar, et al.2026 · 2 citations
20. From Weights to Activations: Is Steering the Next Frontier of Adaptation?Annual Meeting of the Association for Computational Linguistics (ACL)Simon Ostermann, Daniil Gurgurov, Tanja Baeumel, et al.2026 · 1 citation
21. IF-RewardBench: Benchmarking Judge Models for Instruction-Following EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Bosi Wen, Yilin Niu, Cunxiang Wang, et al.2026 · 1 citation
22. Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs' Instruction Following CapabilityFindings of the Association for Computational LinguisticsJiaming Wang, Yunke Zhao, Peng Ding, et al.2026 · 1 citation
23. SCAN: Structured Capability Assessment and Navigation for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Zongqi Wang, Tianle Gu, Chen Gong, et al.2026 · 1 citation
24. AutoChecklist: Composable Pipelines for Checklist Generation and Scoring with LLM-as-a-JudgeAnnual Meeting of the Association for Computational Linguistics (ACL)Karen Zhou, Chenhao Tan2026
25. BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data AugmentationarXiv:2602.23580Yun Wang, Xuansheng Wu, Jingyuan Huang, et al.2026
26. Complex-IF and Beyond: Expert Rubrics for RLVRWorkshop on Generation, Evaluation and Metrics (GEM)Sushant Mehta, Liudas Panavas, Eleanor Fleming, et al.2026
27. DAC-Bench: A Decision-Aware Benchmark for Compositional Mobile GUI TasksAnnual Meeting of the Association for Computational Linguistics (ACL)Yuqing Zhang, Honghui Sheng, Xueyu Hu, et al.2026
28. Deconstructing Instruction-Following: A New Benchmark for Granular Evaluation of Large Language Model Instruction Compliance AbilitiesConference of the European Chapter of the Association for Computational Linguistics (EACL)Alberto Purpura, Li Wang, Sahil Badyal, et al.2026
29. ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction FollowingAnnual Meeting of the Association for Computational Linguistics (ACL)Yuancheng Yang, Lin Yang, Xu Wang, et al.2026
30. InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in SummarizationLanguage Resources and Evaluation Conference (LREC)Kosuke Nishida, Kyosuke Nishida, Itsumi Saito2026
31. PARIF: Pushing the Pareto Frontier of Instruction Following and Reasoning with Curriculum Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Rongchuan Mu, Zexin Wang, Qianyu Wang, et al.2026
32. R ESEARCH QA: Evaluating Scholarly Question Answering at Scale Across 75 Fields with Survey-Mined Questions and RubricsTransactions of the Association for Computational Linguistics (TACL)Li S. Yifei, Allen Chang, Chaitanya Malaviya, et al.2026
33. Shuttle Between Symbolic Instructions and Neural Parameters of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Wangtao Sun, Haotian Xu, Huanxuan Liao, et al.2026
34. SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating ProceduresFindings of the Association for Computational LinguisticsJiaming Wang, Zhe Tang, Zehao Jin, et al.2026
35. We Are What We Repeatedly Do: Improving Long Context Instruction FollowingFindings of the Association for Computational LinguisticsPreston K Robinette, Andrew Hard, Swaroop Ramaswamy, et al.2026

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