--- title: "Who cites MixEval: Deriving Wisdom of the — 45 citing papers" description: "45 papers of the Elamur Research library cite «MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures», found in their…" canonical: "https://research.elamur.ai/free/en/papers/ni-2024-mixeval-deriving-wisdom-crowd-llm/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ni-2024-mixeval-deriving-wisdom-crowd-llm/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [MixEval: Deriving Wisdom of the Crowd from LLM…](https://md.research.elamur.ai/free/en/papers/ni-2024-mixeval-deriving-wisdom-crowd-llm/) / cited by # Who cites «MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures» 45 papers of our library cite it (of the 149,439 whose bibliographies we read) · 15 with a page here, the most cited first · [← back to «MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures»](https://md.research.elamur.ai/free/en/papers/ni-2024-mixeval-deriving-wisdom-crowd-llm/) 1. [**From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline arXiv:2406.11939**Tianle Li, Wei-Lin Chiang, Evan Frick, et al.*2024 · 210 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-crowdsourced-data-high-quality-benchmarks/) 2. [**BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions arXiv:2406.15877**Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, et al.*2024 · 176 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-bigcodebench-benchmarking-code-generation-diverse/) 3. [**JudgeLM: Fine-tuned Large Language Models are Scalable Judges arXiv:2310.17631**Lianghui Zhu, Xinggang Wang, Xinlong Wang*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-judgelm-fine-tuned-large-language/) 4. [**ALLaM: Large Language Models for Arabic and English arXiv:2407.15390**M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/bari-2024-allam-large-language-models-arabic/) 5. **[A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://md.research.elamur.ai/free/en/papers/laskar-2024-systematic-survey-critical-review-evaluating/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, et al.*2024 · 105 citations* 6. **[The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism](https://md.research.elamur.ai/free/en/papers/song-2025-good-bad-greedy-evaluation-llms/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yifan Song, Guoyin Wang, Sujian Li, et al.*2025 · 64 citations* 7. **[Evaluating Language Models as Synthetic Data Generators](https://md.research.elamur.ai/free/en/papers/kim-2025-evaluating-language-models-as-synthetic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Seungone Kim, Juyoung Suk, Xiang Yue, et al.*2025 · 32 citations* 8. [**RMB: Comprehensively Benchmarking Reward Models in LLM Alignment arXiv:2410.09893**Enyu Zhou, Guodong Zheng, Binghai Wang, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-rmb-comprehensively-benchmarking-reward-models/) 9. **[Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment](https://md.research.elamur.ai/free/en/papers/oosterlinck-2025-anchored-preference-optimization-contrastive-revisions/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Karel D'Oosterlinck, Winnie Xu, Chris Develder, et al.*2025 · 20 citations* 10. **[How to Select Datapoints for Efficient Human Evaluation of NLG Models?](https://md.research.elamur.ai/free/en/papers/zouhar-2025-how-select-datapoints-efficient-human/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Vilém Zouhar, Peng Cui, Mrinmaya Sachan*2025 · 17 citations* 11. **[MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems](https://md.research.elamur.ai/free/en/papers/thakur-2025-mirage-bench-automatic-multilingual-benchmark/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nandan Thakur, Suleman Kazi, Ge Luo, et al.*2025 · 15 citations* 12. [**Baichuan Alignment Technical Report arXiv:2410.14940**Mingan Lin, Fan Yang, Yanjun Shen, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-baichuan-alignment-technical-report/) 13. **[Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists](https://md.research.elamur.ai/free/en/papers/pietruszka-2026-can-models-help-us-create/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Michał Pietruszka, Łukasz Borchmann, Aleksander Jędrosz, et al.*2026 · 1 citation* 14. **[Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models](https://md.research.elamur.ai/free/en/papers/yin-2026-decentralized-arena-democratic-scalable-automatic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yanbin Yin, Kun Zhou, Zhen Wang, et al.*2026 · 1 citation* 15. **[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* 30 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/ni-2024-mixeval-deriving-wisdom-crowd-llm/) — MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures