---
title: "Who cites CalibraEval: Calibrating Prediction — 13 citing papers"
description: "13 papers of the Elamur Research library cite «CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges»…"
canonical: "https://research.elamur.ai/free/en/papers/li-2025-calibraeval-calibrating-prediction-distribution-mitigate/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [CalibraEval: Calibrating Prediction Distribution…](https://md.research.elamur.ai/free/en/papers/li-2025-calibraeval-calibrating-prediction-distribution-mitigate/) / cited by

# Who cites «CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges»

13 papers of our library cite it (of the 149,439 whose bibliographies we read) · 4 with a page here, the most cited first · [← back to «CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges»](https://md.research.elamur.ai/free/en/papers/li-2025-calibraeval-calibrating-prediction-distribution-mitigate/)

1. [**LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods arXiv:2412.05579**Haitao Li, Qian Dong, Junjie Chen, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llms-judges-comprehensive-survey-llm/)
2. **[LegalAgentBench: Evaluating LLM Agents in Legal Domain](https://md.research.elamur.ai/free/en/papers/li-2025-legalagentbench-evaluating-llm-agents-legal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haitao Li, Junjie Chen, Jingli Yang, et al.*2025 · 62 citations*
3. **[A Survey on Efficient Large Language Model Training: From Data-centric Perspectives](https://md.research.elamur.ai/free/en/papers/luo-2025-survey-efficient-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyu Luo, Bohan Wu, Xiao Luo, et al.*2025 · 14 citations*
4. **[Rethinking Gender Annotation for Bias Evaluation in Machine Translation: Can LLMs Improve Reliability?](https://md.research.elamur.ai/free/en/papers/manna-2026-rethinking-gender-annotation-bias-evaluation/) [Workshop on Gender-Inclusive Translation Technologies](https://md.research.elamur.ai/free/en/venues/gitt/)**Chiara Manna, Argentina Anna Rescigno, Eva Vanmassenhove*2026*

9 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/li-2025-calibraeval-calibrating-prediction-distribution-mitigate/) — CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges