--- title: "Who cites ProxyQA: An Alternative — 25 citing papers" description: "25 papers of the Elamur Research library cite «ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large…" canonical: "https://research.elamur.ai/free/en/papers/tan-2024-proxyqa-alternative-framework-evaluating-long/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/tan-2024-proxyqa-alternative-framework-evaluating-long/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [ProxyQA: An Alternative Framework for Evaluating…](https://md.research.elamur.ai/free/en/papers/tan-2024-proxyqa-alternative-framework-evaluating-long/) / cited by # Who cites «ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models» 25 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models»](https://md.research.elamur.ai/free/en/papers/tan-2024-proxyqa-alternative-framework-evaluating-long/) 1. [**Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators arXiv:2403.16950**Yinhong Liu, Han Zhou, Zhijiang Guo, et al.*2024 · 76 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-aligning-human-judgement-role-pairwise/) 2. [**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/) 3. [**FormalAlign: Automated Alignment Evaluation for Autoformalization arXiv:2410.10135**Jianqiao Lu, Yingjia Wan, Yinya Huang, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-formalalign-automated-alignment-evaluation-autoformalization/) 4. **[TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review](https://md.research.elamur.ai/free/en/papers/chang-2025-treereview-dynamic-tree-questions-framework/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuan Chang, Ziyue Li, Hengyuan Zhang, et al.*2025 · 16 citations* 5. **[OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking](https://md.research.elamur.ai/free/en/papers/xi-2025-omnithink-expanding-knowledge-boundaries-machine/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zekun Xi, Wenbiao Yin, Jizhan Fang, et al.*2025 · 14 citations* 6. **[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* 7. **[DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking](https://md.research.elamur.ai/free/en/papers/li-2025-deepsolution-boosting-complex-engineering-solution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhuoqun Li, Haiyang Yu, Xuanang Chen, et al.*2025 · 10 citations* 8. **[DebateQA: Evaluating Question Answering on Debatable Knowledge](https://md.research.elamur.ai/free/en/papers/xu-2026-debateqa-evaluating-question-answering-debatable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rongwu Xu, Xuan Qi, Zehan Qi, et al.*2026 · 7 citations* 9. **[OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation](https://md.research.elamur.ai/free/en/papers/zhang-2026-opengenalign-preference-dataset-benchmark-trustworthy/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hanning Zhang, Juntong Song, Juno Zhu, et al.*2026 · 2 citations* 10. **[RoadMapper: A Multi-Agent System for Roadmap Generation of Solving Complex Research Problems](https://md.research.elamur.ai/free/en/papers/liu-2026-roadmapper-multi-agent-system-roadmap/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiacheng Liu, Zichen Tang, Zhongjun Yang, et al.*2026* 15 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/tan-2024-proxyqa-alternative-framework-evaluating-long/) — ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models