---
title: "Who cites Enhancing Factual Consistency of — 67 citing papers"
description: "67 papers of the Elamur Research library cite «Enhancing Factual Consistency of Abstractive Summarization», found in their bibliographies…"
canonical: "https://research.elamur.ai/free/en/papers/zhu-2021-enhancing-factual-consistency-abstractive-summarization/cited-by/"
---
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# Who cites «Enhancing Factual Consistency of Abstractive Summarization»

67 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 «Enhancing Factual Consistency of Abstractive Summarization»](https://md.research.elamur.ai/free/en/papers/zhu-2021-enhancing-factual-consistency-abstractive-summarization/)

1. **[Survey of Hallucination in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,957 citations*
2. **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,396 citations*
3. **[Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 966 citations*
4. **[CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/cao-2021-cliff-contrastive-learning-improving-faithfulness/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shuyang Cao, Lu Wang*2021 · 258 citations*
5. **[Trusting Your Evidence: Hallucinate Less with Context-aware Decoding](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Weijia Shi, Xiaochuang Han, Mike Lewis, et al.*2024 · 240 citations*
6. [**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/)
7. **[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*
8. **[Coreference-Aware Dialogue Summarization](https://md.research.elamur.ai/free/en/papers/liu-2021-coreference-aware-dialogue-summarization/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Zhengyuan Liu, Ke Shi, Nancy Chen*2021 · 81 citations*
9. **[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*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/zhu-2021-enhancing-factual-consistency-abstractive-summarization/) — Enhancing Factual Consistency of Abstractive Summarization
