---
title: "Who cites Understanding Factual Errors in — 79 citing papers"
description: "79 papers of the Elamur Research library cite «Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error…"
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---
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# Who cites «Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors»

79 papers of our library cite it (of the 141,840 whose bibliographies we read) · 5 with a page here, the most cited first · [← back to «Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors»](https://md.research.elamur.ai/free/en/papers/tang-2023-understanding-factual-errors-summarization-errors/)

1. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 178 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/) **[MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liyan Tang, Philippe Laban, Greg Durrett*2024 · 159 citations* [**Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation arXiv:2212.07981**Yixin Liu, Alexander R. Fabbri, Pengfei Liu, et al.*2022 · 116 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-revisiting-gold-standard-grounding-summarization/) **[On Learning to Summarize with Large Language Models as References](https://md.research.elamur.ai/free/en/papers/liu-2024-learning-summarize-large-language-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yixin Liu, Kejian Shi, Katherine He, et al.*2024 · 94 citations* **[HotelCheckSpan: A Benchmark Dataset for LLM Faithfulness](https://md.research.elamur.ai/free/en/papers/schmidtova-2026-hotelcheckspan-benchmark-dataset-llm-faithfulness/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Patricia Schmidtova, Ondrej Dusek, Saad Mahamood*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/tang-2023-understanding-factual-errors-summarization-errors/) — Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors