| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 879 | 2022 |
| The gem benchmark: Natural language generation, its evaluation and metrics S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ... arXiv preprint arXiv:2102.01672, 2021 | 132 | 2021 |
| Low bit-rate speech coding with VQ-VAE and a WaveNet decoder C Gârbacea, A van den Oord, Y Li, FSC Lim, A Luebs, O Vinyals, ... ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 124 | 2019 |
| Neural language generation: Formulation, methods, and evaluation C Garbacea, Q Mei arXiv preprint arXiv:2007.15780, 2020 | 41 | 2020 |
| Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation C Garbacea, S Carton, S Yan, Q Mei Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 26 | 2019 |
| Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification C Garbacea, M Guo, S Carton, Q Mei Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 22 | 2021 |
| Gemv2: Multilingual nlg benchmarking in a single line of code S Gehrmann, A Bhattacharjee, A Mahendiran, A Wang, A Papangelis, ... arXiv preprint arXiv:2206.11249, 2022 | 15 | 2022 |
| Why is constrained neural language generation particularly challenging? C Garbacea, Q Mei arXiv preprint arXiv:2206.05395, 2022 | 12 | 2022 |
| Supporting Exploration of Historical Perspectives across Collections D Odijk, C Gârbacea, T Schoegje, L Hollink, V de Boer, K Ribbens, ... TPDL 2015, 2015 | 11 | 2015 |
| Speech coding using content latent embedding vectors and speaker latent embedding vectors C Garbacea, AGA van den Oord, Y Li, SC Lim, A Luebs, O Vinyals, ... US Patent 11,257,507, 2022 | 8 | 2022 |
| Detecting the reputation polarity of microblog posts C Gârbacea, M Tsagkias, M de Rijke ECAI 2014: 21st European Conference on Artificial Intelligence 263, 339, 2014 | 8 | 2014 |
| Combining multiple signals for semanticizing tweets: University of Amsterdam at# Microposts2015 C Gârbacea, D Odijk, D Graus, I Sijaranamual, M de Rijke WWW’15, 2015 | 6 | 2015 |
| Feature Selection and Data Sampling Methods for Learning Reputation Dimensions C Gârbacea, M Tsagkias, M de Rijke CLEF (Working Notes) 2014, 2014 | 4 | 2014 |
| Neural language generation for content adaptation: Explainable, efficient low-resource text simplification and evaluation GC Garbacea | 3 | 2023 |
| The Web R White, Q Mei, M Rabinovich, P Samarati, O Alonso, B Benatallah, ... ACM Transactions on 16 (3), 2022 | 2 | 2022 |
| The University of Amsterdam (ILPS. UvA) at TREC 2015 Temporal Summarization Track C Gârbacea, E Kanoulas | 2 | 2016 |
| Adapting Pre-trained Language Models to Low-Resource Text Simplification: The Path Matters C Garbacea, Q Mei Conference on Lifelong Learning Agents, 1103-1119, 2022 | 1 | 2022 |
| UMSIForeseer at SemEval-2020 Task 11: Propaganda detection by fine-tuning BERT with resampling and ensemble learning Y Jiang, C Gârbacea, Q Mei Proceedings of the Fourteenth Workshop on Semantic Evaluation, 1841-1846, 2020 | 1 | 2020 |
| An Empirical Study on Explainable Prediction of Text Complexity: Preliminaries for Text Simplification. C Gârbacea, M Guo, S Carton, Q Mei CoRR, 2020 | 1 | 2020 |
| BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling L Gui, C Gârbacea, V Veitch arXiv preprint arXiv:2406.00832, 2024 | | 2024 |
Qiaozhu MeiProfessor, University of MichiganVerified email at umich.edu
