🔗 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»

1. The Curious Case of Neural Text DegenerationarXiv:1904.09751Ari Holtzman, Jan Buys, Li Du, et al.2019 · 2,660 citations
2. GLTR: Statistical Detection and Visualization of Generated TextAnnual Meeting of the Association for Computational Linguistics (ACL)Sebastian Gehrmann, Hendrik Strobelt, Alexander M. Rush2019 · 697 citations
3. Social Bias Frames: Reasoning about Social and Power Implications of LanguageAnnual Meeting of the Association for Computational Linguistics (ACL)Maarten Sap, Saadia Gabriel, Lianhui Qin, et al.2020 · 684 citations
4. Towards a Human-like Open-Domain ChatbotarXiv:2001.09977Daniel Adiwardana, Minh-Thang Luong, David R. So, et al.2020 · 592 citations
5. Defending Against Neural Fake NewsarXiv:1905.12616Rowan Zellers, Ari Holtzman, Hannah Rashkin, et al.2019 · 478 citations
6. CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model CapabilitiesCHI Conference on Human Factors in Computing SystemsMina Lee, Percy Liang, Qian Yang2022 · 423 citations
7. Evaluation of Text Generation: A SurveyarXiv:2006.14799Asli Celikyilmaz, Elizabeth Clark, Jianfeng Gao2020 · 388 citations
8. The GEM Benchmark: Natural Language Generation, its Evaluation and MetricsWorkshop on Generation, Evaluation and Metrics (GEM)Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, et al.2021 · 369 citations
9. FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive SummarizationAnnual Meeting of the Association for Computational Linguistics (ACL)Esin Durmus, He He, Mona Diab2020 · 359 citations
10. Hallucination-Free? Assessing the Reliability of Leading AI Legal Research ToolsarXiv:2405.20362Varun Magesh, Faiz Surani, Matthew Dahl, et al.2024 · 271 citations
11. MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence FrontiersarXiv:2102.01454Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al.2021 · 251 citations
12. Do Massively Pretrained Language Models Make Better Storytellers?Conference on Computational Natural Language Learning (CoNLL)Abigail See, Aneesh Pappu, Rohun Saxena, et al.2019 · 213 citations
13. Sentence Mover's Similarity: Automatic Evaluation for Multi-Sentence TextsAnnual Meeting of the Association for Computational Linguistics (ACL)Elizabeth Clark, Asli Celikyilmaz, Noah A. Smith2019 · 209 citations
14. Large Language Model as Attributed Training Data Generator: A Tale of Diversity and BiasConference on Neural Information Processing Systems (NeurIPS)Yue Yu, Yuchen Zhuang, Jieyu Zhang, et al.2023 · 202 citations
15. A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark DatasetsFindings of the Association for Computational LinguisticsMd 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 TextAnnual Meeting of the Association for Computational Linguistics (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 CurvaturearXiv:2310.05130Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, et al.2023 · 148 citations
18. GLUCOSE: GeneraLized and COntextualized Story ExplanationsNasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, et al.2020 · 145 citations
19. GO FIGURE: A Meta Evaluation of Factuality in SummarizationFindings of the Association for Computational LinguisticsSaadia Gabriel, Asli Celikyilmaz, Rahul Jha, et al.2021 · 130 citations
20. Improved Natural Language Generation via Loss TruncationAnnual Meeting of the Association for Computational Linguistics (ACL)Daniel Kang, Tatsunori B. Hashimoto2020 · 130 citations
21. Evaluating the Evaluation of Diversity in Natural Language GenerationConference of the European Chapter of the Association for Computational Linguistics (EACL)Guy Tevet, Jonathan Berant2021 · 106 citations
22. Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language GenerationConference on Empirical Methods in Natural Language Processing (EMNLP)Mingkai Deng, Bowen Tan, Zhengzhong Liu, et al.2021 · 102 citations
23. Truncation Sampling as Language Model DesmoothingFindings of the Association for Computational LinguisticsJohn Hewitt, Christopher Manning, Percy Liang2022 · 95 citations
24. Trading Off Diversity and Quality in Natural Language GenerationWorkshop on Human Evaluation of NLP Systems (HumEval)Hugh Zhang, Daniel Duckworth, Daphne Ippolito, et al.2021 · 88 citations
25. What Context Features Can Transformer Language Models Use?Annual Meeting of the Association for Computational Linguistics (ACL)Joe O'Connor, Jacob Andreas2021 · 77 citations
26. Measuring the ‘I don't know' Problem through the Lens of Gricean QuantityConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Huda Khayrallah, João Sedoc2021 · 5 citations
27. Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text DetectionAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaowei Zhu, Yubing Ren, Fang Fang, et al.2026
28. Zero-Shot Detection of LLM-Generated Text using Temperature SensitivityAnnual Meeting of the Association for Computational Linguistics (ACL)Shixuan Ma, Jiahao Li, Zhendong Mao, et al.2026

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