---
title: "Who cites FactPEGASUS: Factuality-Aware — 47 citing papers"
description: "47 papers of the Elamur Research library cite «FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization»…"
canonical: "https://research.elamur.ai/free/en/papers/wan-2022-factpegasus-factuality-aware-pre-training/cited-by/"
---
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# Who cites «FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization»

47 papers of our library cite it (of the 141,840 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization»](https://md.research.elamur.ai/free/en/papers/wan-2022-factpegasus-factuality-aware-pre-training/)

1. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/) **[Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies](https://md.research.elamur.ai/free/en/papers/pan-2024-automatically-correcting-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Liangming Pan, Michael Saxon, Wenda Xu, et al.*2024 · 140 citations* **[Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://md.research.elamur.ai/free/en/papers/kumar-2023-language-generation-models-can-cause/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, et al.*2023 · 87 citations*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/wan-2022-factpegasus-factuality-aware-pre-training/) — FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization