---
title: "Who cites Unifying Human and Statistical — 110 citing papers"
description: "110 papers of the Elamur Research library cite «Unifying Human and Statistical Evaluation for Natural Language Generation», found in their…"
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# Who cites «Unifying Human and Statistical Evaluation for Natural Language Generation»

110 papers of our library cite it (of the 149,439 whose bibliographies we read) · 28 with a page here, the most cited first · [← back to «Unifying Human and Statistical Evaluation for Natural Language Generation»](https://md.research.elamur.ai/free/en/papers/hashimoto-2019-unifying-human-statistical-evaluation-natural/)

1. [**The Curious Case of Neural Text Degeneration arXiv:1904.09751**Ari Holtzman, Jan Buys, Li Du, et al.*2019 · 2,660 citations*](https://md.research.elamur.ai/free/en/papers/holtzman-2019-curious-case-neural-text-degeneration/)
2. **[GLTR: Statistical Detection and Visualization of Generated Text](https://md.research.elamur.ai/free/en/papers/gehrmann-2019-gltr-statistical-detection-visualization-generated/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sebastian Gehrmann, Hendrik Strobelt, Alexander M. Rush*2019 · 697 citations*
3. **[Social Bias Frames: Reasoning about Social and Power Implications of Language](https://md.research.elamur.ai/free/en/papers/sap-2020-social-bias-frames-reasoning-about/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Maarten Sap, Saadia Gabriel, Lianhui Qin, et al.*2020 · 684 citations*
4. [**Towards a Human-like Open-Domain Chatbot arXiv:2001.09977**Daniel Adiwardana, Minh-Thang Luong, David R. So, et al.*2020 · 592 citations*](https://md.research.elamur.ai/free/en/papers/adiwardana-2020-towards-human-like-open-domain/)
5. [**Defending Against Neural Fake News arXiv:1905.12616**Rowan Zellers, Ari Holtzman, Hannah Rashkin, et al.*2019 · 478 citations*](https://md.research.elamur.ai/free/en/papers/zellers-2019-defending-against-neural-fake-news/)
6. **[CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://md.research.elamur.ai/free/en/papers/lee-2022-coauthor-designing-human-ai-collaborative/) [CHI Conference on Human Factors in Computing Systems](https://md.research.elamur.ai/free/en/venues/chi-conference-on-human-factors-in-computing-systems/)**Mina Lee, Percy Liang, Qian Yang*2022 · 423 citations*
7. [**Evaluation of Text Generation: A Survey arXiv:2006.14799**Asli Celikyilmaz, Elizabeth Clark, Jianfeng Gao*2020 · 388 citations*](https://md.research.elamur.ai/free/en/papers/celikyilmaz-2020-evaluation-text-generation-survey/)
8. **[The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://md.research.elamur.ai/free/en/papers/gehrmann-2021-gem-benchmark-natural-language-generation/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, et al.*2021 · 369 citations*
9. **[FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/durmus-2020-feqa-question-answering-evaluation-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Esin Durmus, He He, Mona Diab*2020 · 359 citations*
10. [**Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools arXiv:2405.20362**Varun Magesh, Faiz Surani, Matthew Dahl, et al.*2024 · 271 citations*](https://md.research.elamur.ai/free/en/papers/magesh-2024-hallucination-free-assessing-reliability-leading/)
11. [**MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers arXiv:2102.01454**Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al.*2021 · 251 citations*](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/)
12. **[Do Massively Pretrained Language Models Make Better Storytellers?](https://md.research.elamur.ai/free/en/papers/see-2019-massively-pretrained-language-models-make/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Abigail See, Aneesh Pappu, Rohun Saxena, et al.*2019 · 213 citations*
13. **[Sentence Mover's Similarity: Automatic Evaluation for Multi-Sentence Texts](https://md.research.elamur.ai/free/en/papers/clark-2019-sentence-mover-s-similarity-automatic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Elizabeth Clark, Asli Celikyilmaz, Noah A. Smith*2019 · 209 citations*
14. **[Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://md.research.elamur.ai/free/en/papers/yu-2023-large-language-model-attributed-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yue Yu, Yuchen Zhuang, Jieyu Zhang, et al.*2023 · 202 citations*
15. **[A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 200 citations*
16. **[Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text](https://md.research.elamur.ai/free/en/papers/dou-2022-is-gpt-3-text-indistinguishable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, et al.*2022 · 152 citations*
17. [**Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature arXiv:2310.05130**Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, et al.*2023 · 148 citations*](https://md.research.elamur.ai/free/en/papers/bao-2023-fast-detectgpt-efficient-zero-shot/)
18. [**GLUCOSE: GeneraLized and COntextualized Story Explanations**Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, et al.*2020 · 145 citations*](https://md.research.elamur.ai/free/en/papers/mostafazadeh-2020-glucose-generalized-contextualized-story-explanations/)
19. **[GO FIGURE: A Meta Evaluation of Factuality in Summarization](https://md.research.elamur.ai/free/en/papers/gabriel-2021-go-figure-meta-evaluation-factuality/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, et al.*2021 · 130 citations*
20. **[Improved Natural Language Generation via Loss Truncation](https://md.research.elamur.ai/free/en/papers/kang-2020-improved-natural-language-generation-loss/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daniel Kang, Tatsunori B. Hashimoto*2020 · 130 citations*
21. **[Evaluating the Evaluation of Diversity in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/tevet-2021-evaluating-evaluation-diversity-natural-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Guy Tevet, Jonathan Berant*2021 · 106 citations*
22. **[Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation](https://md.research.elamur.ai/free/en/papers/deng-2021-compression-transduction-creation-unified-framework/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mingkai Deng, Bowen Tan, Zhengzhong Liu, et al.*2021 · 102 citations*
23. **[Truncation Sampling as Language Model Desmoothing](https://md.research.elamur.ai/free/en/papers/hewitt-2022-truncation-sampling-as-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**John Hewitt, Christopher Manning, Percy Liang*2022 · 95 citations*
24. **[Trading Off Diversity and Quality in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/zhang-2021-trading-off-diversity-quality-natural/) [Workshop on Human Evaluation of NLP Systems (HumEval)](https://md.research.elamur.ai/free/en/venues/humeval/)**Hugh Zhang, Daniel Duckworth, Daphne Ippolito, et al.*2021 · 88 citations*
25. **[What Context Features Can Transformer Language Models Use?](https://md.research.elamur.ai/free/en/papers/connor-2021-what-context-features-can-transformer/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joe O'Connor, Jacob Andreas*2021 · 77 citations*
26. **[Measuring the ‘I don't know' Problem through the Lens of Gricean Quantity](https://md.research.elamur.ai/free/en/papers/khayrallah-2021-measuring-i-don-t-know/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Huda Khayrallah, João Sedoc*2021 · 5 citations*
27. **[Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detection](https://md.research.elamur.ai/free/en/papers/zhu-2026-exons-detect-identifying-amplifying-exonic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaowei Zhu, Yubing Ren, Fang Fang, et al.*2026*
28. **[Zero-Shot Detection of LLM-Generated Text using Temperature Sensitivity](https://md.research.elamur.ai/free/en/papers/ma-2026-zero-shot-detection-llm-generated/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shixuan Ma, Jiahao Li, Zhendong Mao, et al.*2026*

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

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