| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 3497 | 2021 |
| Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022 | 803 | 2022 |
| Using deep learning to enhance cancer diagnosis and classification R Fakoor, F Ladhak, A Nazi, M Huber The 30th International Conference on Machine Learning (ICML 2013), WHEALTH …, 2013 | 640 | 2013 |
| Benchmarking large language models for news summarization T Zhang, F Ladhak, E Durmus, P Liang, K McKeown, TB Hashimoto Transactions of the Association for Computational Linguistics 12, 39-57, 2024 | 245 | 2024 |
| Whose opinions do language models reflect? S Santurkar, E Durmus, F Ladhak, C Lee, P Liang, T Hashimoto International Conference on Machine Learning, 29971-30004, 2023 | 214 | 2023 |
| WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization F Ladhak, E Durmus, C Cardie, K McKeown Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 177 | 2020 |
| Easily accessible text-to-image generation amplifies demographic stereotypes at large scale F Bianchi, P Kalluri, E Durmus, F Ladhak, M Cheng, D Nozza, ... Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 176 | 2023 |
| 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 |
| A neural interlingua for multilingual machine translation Y Lu, P Keung, F Ladhak, V Bhardwaj, S Zhang, J Sun Proceedings of the Third Conference on Machine Translation: Research Papers …, 2018 | 113 | 2018 |
| Lattice encoding using recurrent neural networks F Ladhak, A Gandhe, M Dreyer, A Rastrow, B Hoffmeister, L Mathias US Patent 10,176,802, 2019 | 108 | 2019 |
| Evaluating human-language model interaction M Lee, M Srivastava, A Hardy, J Thickstun, E Durmus, A Paranjape, ... arXiv preprint arXiv:2212.09746, 2022 | 79 | 2022 |
| LATTICE RNN: Recurrent neural networks over lattices F Ladhak, A Gandhe, M Dreyer, L Mathias, A Rastrow, B Hoffmeister | 71 | 2016 |
| Faithful or extractive? on mitigating the faithfulness-abstractiveness trade-off in abstractive summarization F Ladhak, E Durmus, H He, C Cardie, K McKeown arXiv preprint arXiv:2108.13684, 2021 | 64 | 2021 |
| Proving test set contamination in black box language models Y Oren, N Meister, N Chatterji, F Ladhak, TB Hashimoto arXiv preprint arXiv:2310.17623, 2023 | 51 | 2023 |
| Exploring Content Selection in Summarization of Novel Chapters F Ladhak, B Li, Y Al-Onaizan, K McKeown Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 37 | 2020 |
| When do pre-training biases propagate to downstream tasks? a case study in text summarization F Ladhak, E Durmus, M Suzgun, T Zhang, D Jurafsky, K McKeown, ... Proceedings of the 17th Conference of the European Chapter of the …, 2023 | 31 | 2023 |
| Spurious Correlations in Reference-Free Evaluation of Text Generation E Durmus, F Ladhak, T Hashimoto arXiv preprint arXiv:2204.09890, 2022 | 27 | 2022 |
| From sparse to dense: GPT-4 summarization with chain of density prompting G Adams, A Fabbri, F Ladhak, E Lehman, N Elhadad arXiv preprint arXiv:2309.04269, 2023 | 26 | 2023 |
| The role of pragmatic and discourse context in determining argument impact E Durmus, F Ladhak, C Cardie Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 26 | 2019 |
| To BERT or not to BERT: Comparing task-specific and task-agnostic semi-supervised approaches for sequence tagging K Bhattacharjee, M Ballesteros, R Anubhai, S Muresan, J Ma, F Ladhak, ... arXiv preprint arXiv:2010.14042, 2020 | 22 | 2020 |
Esin DurmusAnthropicVerified email at stanford.edu
