Amanda Bertsch

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Graham NeubigCarnegie Mellon University, All Hands AIVerified email at cs.cmu.edu
Matthew R. GormleyCarnegie Mellon UniversityVerified email at cs.cmu.edu
Pedro Henrique MartinsUnbabelVerified email at tecnico.ulisboa.pt
Aman MadaanCarnegie Mellon UniversityVerified email at cs.cmu.edu
Patrick FernandesCarnegie Mellon University & Instituto Superior TécnicoVerified email at cs.cmu.edu
Shuyan ZhouCarnegie Mellon UniversityVerified email at cs.cmu.edu
Emmy LiuPhD Student, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
António FarinhasInstituto Superior Técnico, Universidade de Lisboa, PortugalVerified email at tecnico.ulisboa.pt
Andre MartinsCarnegie Mellon University and Instituto Superior TecnicoVerified email at cs.cmu.edu
Emma StrubellAssistant Professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Steven BethardAssociate Professor, University of ArizonaVerified email at arizona.edu
Alan W BlackProfessor, Language Technologies Institute, Carnegie Mellon UniversityVerified email at cs.cmu.edu

Amanda Bertsch

PhD student, Language Technologies Institute, Carnegie Mellon University

Verified email at cs.cmu.edu - Homepage

summarization long-context NLU conditional generation NLP


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Unlimiformer: Long-range transformers with unlimited length input A Bertsch, U Alon, G Neubig, MR Gormley NeurIPS 2023, 2023	94	2023
Bridging the gap: A survey on integrating (human) feedback for natural language generation P Fernandes, A Madaan, E Liu, A Farinhas, PH Martins, A Bertsch, ... Transactions of the Association of Computational Linguistics, 2023	64	2023
Llms as workers in human-computational algorithms? replicating crowdsourcing pipelines with llms T Wu, H Zhu, M Albayrak, A Axon, A Bertsch, W Deng, Z Ding, B Guo, ... arXiv preprint arXiv:2307.10168, 2023	21	2023
Prompt2Model: Generating Deployable Models from Natural Language Instructions V Viswanathan, C Zhao, A Bertsch, T Wu, G Neubig EMNLP Demo, 2023	19	2023
In-context learning with long-context models: An in-depth exploration A Bertsch, M Ivgi, U Alon, J Berant, MR Gormley, G Neubig arXiv preprint arXiv:2405.00200, 2024	18	2024
It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk A Bertsch, A Xie, G Neubig, MR Gormley arXiv preprint arXiv:2310.01387, 2023	11	2023
Summqa at mediqa-chat 2023: In-context learning with gpt-4 for medical summarization Y Mathur, S Rangreji, R Kapoor, M Palavalli, A Bertsch, MR Gormley arXiv preprint arXiv:2306.17384, 2023	11	2023
Detection of puffery on the english wikipedia A Bertsch, S Bethard Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021 …, 2021	9	2021
To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing S Gururaja, A Bertsch, C Na, DG Widder, E Strubell arXiv preprint arXiv:2310.07715, 2023	8	2023
He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues A Bertsch, G Neubig, MR Gormley Findings of EMNLP, 2022	6	2022
Evaluating gender bias transfer from film data A Bertsch, A Oh, S Natu, S Gangu, AW Black, E Strubell Proceedings of the 4th Workshop on Gender Bias in Natural Language …, 2022	2	2022
From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models S Welleck, A Bertsch, M Finlayson, H Schoelkopf, A Xie, G Neubig, ... arXiv preprint arXiv:2406.16838, 2024		2024
Deep Learning for Bias Detection on the English Wikipedia AL BERTSCH The University of Arizona., 2021		2021

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