--- title: "Who cites Benchmarking Generation and — 56 citing papers" description: "56 papers of the Elamur Research library cite «Benchmarking Generation and Evaluation Capabilities of Large Language Models for…" canonical: "https://research.elamur.ai/free/en/papers/liu-2023-benchmarking-generation-evaluation-capabilities-large/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/liu-2023-benchmarking-generation-evaluation-capabilities-large/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Benchmarking Generation and Evaluation…](https://md.research.elamur.ai/free/en/papers/liu-2023-benchmarking-generation-evaluation-capabilities-large/) / cited by # Who cites «Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization» 56 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization»](https://md.research.elamur.ai/free/en/papers/liu-2023-benchmarking-generation-evaluation-capabilities-large/) 1. **[On Learning to Summarize with Large Language Models as References](https://md.research.elamur.ai/free/en/papers/liu-2024-learning-summarize-large-language-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yixin Liu, Kejian Shi, Katherine He, et al.*2024 · 94 citations* 2. **[Large Language Model Instruction Following: A Survey of Progresses and Challenges](https://md.research.elamur.ai/free/en/papers/lou-2024-large-language-model-instruction-following/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Renze Lou, Kai Zhang, Wenpeng Yin*2024 · 83 citations* 3. [**AgentInstruct: Toward Generative Teaching with Agentic Flows arXiv:2407.03502**Arindam Mitra, Luciano Del Corro, Guoqing Zheng, et al.*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2024-agentinstruct-toward-generative-teaching-agentic/) 4. **[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* 5. [**Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges arXiv:2403.02990**Bosheng Ding, Chengwei Qin, Ruochen Zhao, et al.*2024 · 32 citations*](https://md.research.elamur.ai/free/en/papers/ding-2024-data-augmentation-using-large-language/) 6. **[Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual Settings](https://md.research.elamur.ai/free/en/papers/xu-2025-does-context-matter-contextualjudgebench-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Austin Xu, Srijan Bansal, Yifei Ming, et al.*2025 · 14 citations* 7. **[Measuring and Modifying the Readability of English Texts with GPT-4](https://md.research.elamur.ai/free/en/papers/trott-2024-measuring-modifying-readability-english-texts/) [Workshop on Text Simplification, Accessibility and Readability](https://md.research.elamur.ai/free/en/venues/tsar/)**Sean Trott, Pamela Rivière*2024 · 14 citations* 8. **[Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond](https://md.research.elamur.ai/free/en/papers/sanni-2025-afrispeech-dialog-benchmark-dataset-spontaneous/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Mardhiyah Sanni, Tassallah Abdullahi, Devendra Deepak Kayande, et al.*2025 · 11 citations* 9. **[MDCure: A Scalable Pipeline for Multi-Document Instruction-Following](https://md.research.elamur.ai/free/en/papers/liu-2025-mdcure-scalable-pipeline-multi-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu, et al.*2025 · 11 citations* 10. **[SciArena: An Open Evaluation Platform for Non-Verifiable Scientific Literature-Grounded Tasks](https://md.research.elamur.ai/free/en/papers/zhao-2025-sciarena-open-evaluation-platform-non/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yilun Zhao, Kaiyan Zhang, Tiansheng Hu, et al.*2025 · 11 citations* 11. **[AbGen: Evaluating Large Language Models in Ablation Study Design and Evaluation for Scientific Research](https://md.research.elamur.ai/free/en/papers/zhao-2025-abgen-evaluating-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yilun Zhao, Weiyuan Chen, Zhijian Xu, et al.*2025 · 9 citations* 12. **[J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization](https://md.research.elamur.ai/free/en/papers/xu-2026-j4r-learning-judge-equivalent-initial/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Austin Xu, Yilun Zhou, Xuan-Phi Nguyen, et al.*2026 · 3 citations* 13. **[Exploring Iterative Controllable Summarization with Large Language Models](https://md.research.elamur.ai/free/en/papers/ryu-2026-exploring-iterative-controllable-summarization-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sangwon Ryu, Heejin Do, Daehui Kim, et al.*2026 · 2 citations* 14. **[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* 42 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/liu-2023-benchmarking-generation-evaluation-capabilities-large/) — Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization