🔗 Who cites «“Seeing the Big through the Small”: Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?»

16 papers of our library cite it (of the 149,439 whose bibliographies we read) · 5 with a page here, the most cited first · ← back to «“Seeing the Big through the Small”: Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?»

1. The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Nitay Calderon, Roi Reichart, Rotem Dror2025 · 52 citations
2. A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLIFindings of the Association for Computational LinguisticsBeiduo Chen, Siyao Peng, Anna Korhonen, et al.2025 · 9 citations
3. LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language InferenceConference on Empirical Methods in Natural Language Processing (EMNLP)Pingjun Hong, Beiduo Chen, Siyao Peng, et al.2025 · 8 citations
4. Label and Explanation Variation in LLM-Based Annotation: a Case Study in Natural Language InferenceAnnual Meeting of the Association for Computational Linguistics (ACL)Artur Kulmizev, Erika Lombart, Patrick Watrin, et al.2026 · 2 citations
5. EVADE: LLM-Based Explanation Generation and Validation for Error Detection in NLIFindings of the Association for Computational LinguisticsLongfei Zuo, Barbara Plank, Siyao Peng2026 · 1 citation

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

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