---
title: "Who cites Improving Faithfulness in — 66 citing papers"
description: "66 papers of the Elamur Research library cite «Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and…"
canonical: "https://research.elamur.ai/free/en/papers/chen-2021-improving-faithfulness-abstractive-summarization-contrast/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Improving Faithfulness in Abstractive…](https://md.research.elamur.ai/free/en/papers/chen-2021-improving-faithfulness-abstractive-summarization-contrast/) / cited by

# Who cites «Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection»

66 papers of our library cite it (of the 141,840 whose bibliographies we read) · 9 with a page here, the most cited first · [← back to «Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection»](https://md.research.elamur.ai/free/en/papers/chen-2021-improving-faithfulness-abstractive-summarization-contrast/)

1. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 220 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/) **[Measure and Improve Robustness in NLP Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2022-measure-improve-robustness-nlp-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xuezhi Wang, Haohan Wang, Diyi Yang*2022 · 148 citations* [**A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation arXiv:2307.03987**Neeraj Varshney; Wenlin Yao; Hongming Zhang; Jianshu Chen;, Dong Yu*2023 · 139 citations*](https://md.research.elamur.ai/free/en/papers/varshney-2023-stitch-time-saves-nine-detecting/) **[Improving Factual Consistency of Abstractive Summarization via Question Answering](https://md.research.elamur.ai/free/en/papers/nan-2021-improving-factual-consistency-abstractive-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, et al.*2021 · 128 citations* **[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/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liyan Tang, Tanya Goyal, Alex Fabbri, et al.*2023 · 126 citations* **[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/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**David Wan, Mohit Bansal*2022 · 101 citations* **[Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)](https://md.research.elamur.ai/free/en/papers/liu-2024-hallucinations-large-language/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Zeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.*2024*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/chen-2021-improving-faithfulness-abstractive-summarization-contrast/) — Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection