---
title: "Who cites Refining Input Guardrails: — 8 citing papers"
description: "8 papers of the Elamur Research library cite «Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought…"
canonical: "https://research.elamur.ai/free/en/papers/rad-2025-refining-input-guardrails-enhancing-llm/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Refining Input Guardrails: Enhancing…](https://md.research.elamur.ai/free/en/papers/rad-2025-refining-input-guardrails-enhancing-llm/) / cited by

# Who cites «Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment»

8 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 «Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment»](https://md.research.elamur.ai/free/en/papers/rad-2025-refining-input-guardrails-enhancing-llm/)

1. **[ART: Adaptive Reasoning Trees for Explainable Claim Verification](https://md.research.elamur.ai/free/en/papers/wadhwa-2026-art-adaptive-reasoning-trees-explainable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sahil Wadhwa, Himanshu Kumar, Guanqun Yang, et al.*2026 · 1 citation*
2. **[SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models](https://md.research.elamur.ai/free/en/papers/nghiem-2026-smarter-data-efficient-framework-improve/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huy Nghiem, Advik Sachdeva, Hal Daumé Iii*2026 · 1 citation*
3. **[Choose Your Lens: Multi-Perspective Value Alignment of Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/zhao-2026-choose-your-lens-multi-perspective/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Gejian Zhao, Hanzhou Wu, Xinpeng Zhang*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/rad-2025-refining-input-guardrails-enhancing-llm/) — Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment