---
title: "Who cites Influences on LLM Calibration: A — 3 citing papers"
description: "3 papers of the Elamur Research library cite «Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt…"
canonical: "https://research.elamur.ai/free/en/papers/xia-2025-influences-llm-calibration-study-response/cited-by/"
---
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# Who cites «Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles»

3 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles»](https://md.research.elamur.ai/free/en/papers/xia-2025-influences-llm-calibration-study-response/)

1. **[Label and Explanation Variation in LLM-Based Annotation: a Case Study in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/kulmizev-2026-label-explanation-variation-llm-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Artur Kulmizev, Erika Lombart, Patrick Watrin, et al.*2026 · 2 citations*
2. **[BaseCal: Unsupervised Confidence Calibration via Base Model Signals](https://md.research.elamur.ai/free/en/papers/tan-2026-basecal-unsupervised-confidence-calibration-base/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hexiang Tan, Wanli Yang, Junwei Zhang, et al.*2026 · 1 citation*
3. **[EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/yeom-2026-epicar-knowing-what-you-don/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jewon Yeom, Jaewon Sok, Seonghyeon Park, et al.*2026*
- [← back to](https://md.research.elamur.ai/free/en/papers/xia-2025-influences-llm-calibration-study-response/) — Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles