---
title: "Who cites Xuansheng Wu — 115 papers of our library"
description: "115 papers of the Elamur Research library cite the works of Xuansheng Wu, found in their bibliographies; the most cited first."
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---
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# 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](https://md.research.elamur.ai/free/en/authors/xuansheng-wu/)

1. [**WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild arXiv:2406.04770**Bill Yuchen Lin, Yuntian Deng, Khyathi Chandu, et al.*2024 · 105 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-wildbench-benchmarking-llms-challenging-tasks/)
2. **[Generalizing Verifiable Instruction Following](https://md.research.elamur.ai/free/en/papers/pyatkin-2025-generalizing-verifiable-instruction-following/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Valentina Pyatkin, Saumya Malik, Victoria Graf, et al.*2025 · 59 citations*
3. [**Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language Models arXiv:2404.02823**Haoran Sun, Lixin Liu, Junjie Li, et al.*2024 · 40 citations*](https://md.research.elamur.ai/free/en/papers/sun-2024-conifer-improving-complex-constrained-instruction/)
4. **[Checklists Are Better Than Reward Models For Aligning Language Models](https://md.research.elamur.ai/free/en/papers/viswanathan-2025-checklists-better-than-reward-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Vijay Viswanathan, Yanchao Sun, Shuang Ma, et al.*2025 · 37 citations*
5. **[Direct Judgement Preference Optimization](https://md.research.elamur.ai/free/en/papers/wang-2025-direct-judgement-preference-optimization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**PeiFeng Wang, Austin Xu, Yilun Zhou, et al.*2025 · 35 citations*
6. **[CFBench: A Comprehensive Constraints-Following Benchmark for LLMs](https://md.research.elamur.ai/free/en/papers/zhang-2025-cfbench-comprehensive-constraints-following-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tao Zhang, ChengLIn Zhu, Yanjun Shen, et al.*2025 · 30 citations*
7. **[OpenRubrics: Towards Scalable Synthetic Rubric Generation for Reward Modeling and LLM Alignment](https://md.research.elamur.ai/free/en/papers/liu-2026-openrubrics-scalable-synthetic-rubric-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianci Liu, Ran Xu, Tony Yu, et al.*2026 · 25 citations*
8. **[IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization](https://md.research.elamur.ai/free/en/papers/zhang-2025-iopo-empowering-llms-complex-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinghua Zhang, Haiyang Yu, Cheng Fu, et al.*2025 · 14 citations*
9. **[Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/fu-2026-scaling-reasoning-losing-control-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Scenarios](https://md.research.elamur.ai/free/en/papers/wu-2025-lifbench-evaluating-instruction-following-performance/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaodong Wu, Minhao Wang, Yichen Liu, et al.*2025 · 11 citations*
11. **[AdvancedIF: Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following](https://md.research.elamur.ai/free/en/papers/he-2026-advancedif-rubric-based-benchmarking-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yun He, Wenzhe Li, Hejia Zhang, et al.*2026 · 9 citations*
12. **[UltraIF: Advancing Instruction Following from the Wild](https://md.research.elamur.ai/free/en/papers/an-2025-ultraif-advancing-instruction-following-wild/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kaikai An, Li Sheng, Ganqu Cui, et al.*2025 · 7 citations*
13. **[What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts](https://md.research.elamur.ai/free/en/papers/yang-2026-what-prompts-don-t-say/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenyang Yang, Yike Shi, Qianou Ma, et al.*2026 · 6 citations*
14. **[EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/zou-2025-eifbench-extremely-complex-instruction-following/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/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 Models](https://md.research.elamur.ai/free/en/papers/ye-2026-muldimif-multi-dimensional-constraint-framework/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Junjie Ye, Caishuang Huang, Zhuohan Chen, et al.*2026 · 4 citations*
16. **[OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding](https://md.research.elamur.ai/free/en/papers/ding-2026-octobench-benchmarking-scaffold-aware-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Deming Ding, Shichun Liu, Enhui Yang, et al.*2026 · 4 citations*
17. **[Spotlight Your Instructions: Instruction-following with Dynamic Attention Steering](https://md.research.elamur.ai/free/en/papers/venkateswaran-2026-spotlight-your-instructions-instruction-following/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Praveen Venkateswaran, Danish Contractor*2026 · 4 citations*
18. **[MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training](https://md.research.elamur.ai/free/en/papers/huang-2025-musc-improving-complex-instruction-following/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hui Huang, Jiaheng Liu, Yancheng He, et al.*2025 · 4 citations*
19. **[KCIF: Knowledge-Conditioned Instruction Following](https://md.research.elamur.ai/free/en/papers/murthy-2026-kcif-knowledge-conditioned-instruction-following/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Rudra Murthy, Praveen Venkateswaran, Prince Kumar, et al.*2026 · 2 citations*
20. **[From Weights to Activations: Is Steering the Next Frontier of Adaptation?](https://md.research.elamur.ai/free/en/papers/ostermann-2026-weights-activations-is-steering-next/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Simon Ostermann, Daniil Gurgurov, Tanja Baeumel, et al.*2026 · 1 citation*
21. **[IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation](https://md.research.elamur.ai/free/en/papers/wen-2026-if-rewardbench-benchmarking-judge-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Capability](https://md.research.elamur.ai/free/en/papers/wang-2026-meeseeks-feedback-driven-iterative-self/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiaming Wang, Yunke Zhao, Peng Ding, et al.*2026 · 1 citation*
23. **[SCAN: Structured Capability Assessment and Navigation for LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-scan-structured-capability-assessment-navigation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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-Judge](https://md.research.elamur.ai/free/en/papers/zhou-2026-autochecklist-composable-pipelines-checklist-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Karen Zhou, Chenhao Tan*2026*
25. [**BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation arXiv:2602.23580**Yun Wang, Xuansheng Wu, Jingyuan Huang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-bridge-gap-mitigating-bias-amplification/)
26. **[Complex-IF and Beyond: Expert Rubrics for RLVR](https://md.research.elamur.ai/free/en/papers/mehta-2026-complex-if-beyond-expert-rubrics/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Sushant Mehta, Liudas Panavas, Eleanor Fleming, et al.*2026*
27. **[DAC-Bench: A Decision-Aware Benchmark for Compositional Mobile GUI Tasks](https://md.research.elamur.ai/free/en/papers/zhang-2026-dac-bench-decision-aware-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Abilities](https://md.research.elamur.ai/free/en/papers/purpura-2026-deconstructing-instruction-following-new-benchmark/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Alberto Purpura, Li Wang, Sahil Badyal, et al.*2026*
29. **[ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following](https://md.research.elamur.ai/free/en/papers/yang-2026-imprif-stronger-implicit-reasoning-leads/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuancheng Yang, Lin Yang, Xu Wang, et al.*2026*
30. **[InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in Summarization](https://md.research.elamur.ai/free/en/papers/nishida-2026-instructsum-benchmark-evaluate-instruction-following/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Kosuke Nishida, Kyosuke Nishida, Itsumi Saito*2026*
31. **[PARIF: Pushing the Pareto Frontier of Instruction Following and Reasoning with Curriculum Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/mu-2026-parif-pushing-pareto-frontier-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 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*
33. **[Shuttle Between Symbolic Instructions and Neural Parameters of Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2026-shuttle-between-symbolic-instructions-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wangtao Sun, Haotian Xu, Huanxuan Liao, et al.*2026*
34. **[SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures](https://md.research.elamur.ai/free/en/papers/wang-2026-sop-maze-evaluating-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiaming Wang, Zhe Tang, Zehao Jin, et al.*2026*
35. **[We Are What We Repeatedly Do: Improving Long Context Instruction Following](https://md.research.elamur.ai/free/en/papers/robinette-2026-we-are-what-we-repeatedly/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Preston K Robinette, Andrew Hard, Swaroop Ramaswamy, et al.*2026*

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

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