--- title: "Who cites LLM Comparative Assessment: — 66 citing papers" description: "66 papers of the Elamur Research library cite «LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons…" canonical: "https://research.elamur.ai/free/en/papers/liusie-2024-llm-comparative-assessment-zero-shot/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/liusie-2024-llm-comparative-assessment-zero-shot/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [LLM Comparative Assessment: Zero-shot NLG…](https://md.research.elamur.ai/free/en/papers/liusie-2024-llm-comparative-assessment-zero-shot/) / cited by # Who cites «LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models» 66 papers of our library cite it (of the 141,840 whose bibliographies we read) · 4 with a page here, the most cited first · [← back to «LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models»](https://md.research.elamur.ai/free/en/papers/liusie-2024-llm-comparative-assessment-zero-shot/) 1. [**LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks arXiv:2406.18403**Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/bavaresco-2024-llms-instead-human-judges-large/) 2. [**Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators arXiv:2403.16950**Yinhong Liu, Han Zhou, Zhijiang Guo, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-aligning-human-judgement-role-pairwise/) 62 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/liusie-2024-llm-comparative-assessment-zero-shot/) — LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models