| 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 | 2844 | 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 | 639 | 2022 |
| FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization E Durmus, H He, M Diab ACL, 2020 | 354 | 2020 |
| 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 | 180 | 2024 |
| WikiLingua: A new benchmark dataset for cross-lingual abstractive summarization F Ladhak, E Durmus, C Cardie, K McKeown arXiv preprint arXiv:2010.03093, 2020 | 161 | 2020 |
| 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 | 149 | 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 | 136 | 2021 |
| 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 | 126 | 2023 |
| Exploring the role of prior beliefs for argument persuasion E Durmus, C Cardie NAACL, 2018 | 72 | 2018 |
| Towards measuring the representation of subjective global opinions in language models E Durmus, K Nyugen, TI Liao, N Schiefer, A Askell, A Bakhtin, C Chen, ... arXiv preprint arXiv:2306.16388, 2023 | 62 | 2023 |
| Evaluating human-language model interaction M Lee, M Srivastava, A Hardy, J Thickstun, E Durmus, A Paranjape, ... arXiv preprint arXiv:2212.09746, 2022 | 62 | 2022 |
| 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 | 60 | 2021 |
| Marked personas: Using natural language prompts to measure stereotypes in language models M Cheng, E Durmus, D Jurafsky arXiv preprint arXiv:2305.18189, 2023 | 52 | 2023 |
| Studying large language model generalization with influence functions R Grosse, J Bae, C Anil, N Elhage, A Tamkin, A Tajdini, B Steiner, D Li, ... arXiv preprint arXiv:2308.03296, 2023 | 45 | 2023 |
| Towards understanding sycophancy in language models M Sharma, M Tong, T Korbak, D Duvenaud, A Askell, SR Bowman, ... arXiv preprint arXiv:2310.13548, 2023 | 39 | 2023 |
| Exploring the Role of Argument Structure in Online Debate Persuasion J Li, E Durmus, C Cardie EMNLP, 2020 | 37 | 2020 |
| Measuring faithfulness in chain-of-thought reasoning T Lanham, A Chen, A Radhakrishnan, B Steiner, C Denison, ... arXiv preprint arXiv:2307.13702, 2023 | 34 | 2023 |
| Persuasion of the Undecided: Language vs. the Listener. L Longpre, E Durmus, C Cardie Proceedings of the 6th Workshop on Argument Mining, 2019 | 34 | 2019 |
| Question decomposition improves the faithfulness of model-generated reasoning A Radhakrishnan, K Nguyen, A Chen, C Chen, C Denison, D Hernandez, ... arXiv preprint arXiv:2307.11768, 2023 | 33* | 2023 |
| A corpus for modeling user and language effects in argumentation on online debating E Durmus, C Cardie ACL, 2019 | 29 | 2019 |
Faisal LadhakColumbia UniversityVerified email at columbia.edu
