---
title: "Who cites The Alternative Annotator Test for — 29 citing papers"
description: "29 papers of the Elamur Research library cite «The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing…"
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# Who cites «The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs»

29 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · [← back to «The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs»](https://md.research.elamur.ai/free/en/papers/calderon-2025-alternative-annotator-test-llm-as/)

1. **[Enhancing Automated Interpretability with Output-Centric Feature Descriptions](https://md.research.elamur.ai/free/en/papers/arieh-2025-enhancing-automated-interpretability-output-centric/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yoav Gur-Arieh, Roy Mayan, Chen Agassy, et al.*2025 · 13 citations*
2. **[Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection](https://md.research.elamur.ai/free/en/papers/stacey-2026-improving-ood-performance-closed-source/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Joe Stacey, Lisa Alazraki, Aran Ubhi, et al.*2026 · 1 citation*
3. **[When LLMs Annotate: Reliability Challenges in Low-Resource NLI](https://md.research.elamur.ai/free/en/papers/panahi-2026-when-llms-annotate-reliability-challenges/) [Workshop on Language Models for Low-Resource Languages](https://md.research.elamur.ai/free/en/venues/loreslm/)**Solmaz Panahi, John Kelleher, Vasudevan Nedumpozhimana*2026 · 1 citation*
4. [**BenGER: Benchmarking LLM Systems on Subsumption-Based Legal Reasoning in German Law arXiv:2605.28183**Sebastian Nagl, Ann-Kristin Mayrhofer, Martin Heidebach, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nagl-2026-benger-benchmarking-llm-systems-subsumption/)
5. [**Don't ‘Well, Actually' Me Unless You Know What You're Talking About: Weak Presupposition Verification Degrades General QA Performance arXiv:2608.06539**Shenran Wang, Vered Shwartz, Hila Gonen*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-don-t-well-actually-me/)
6. **[How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting](https://md.research.elamur.ai/free/en/papers/seegmiller-2026-how-much-would-clinician-edit/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Parker Seegmiller, Joseph Gatto, Sarah E. Greer, et al.*2026*
7. **[Human vs LLM in Conversational Repair Annotation: A New Resource and Comparative Study](https://md.research.elamur.ai/free/en/papers/ngo-2026-human-vs-llm-conversational-repair/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Anh Ngo, Nicolas Rollet, Catherine Pelachaud, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/calderon-2025-alternative-annotator-test-llm-as/) — The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs