Detection of Puffery on the English Wikipedia. - Amanda Bertsch; Steven Bethard (W-NUT, 2021)

He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues. - Amanda Bertsch; Graham Neubig; Matthew R. Gormley (EMNLP (Findings), 2022)

SummQA at MEDIQA-Chat 2023: In-Context Learning with GPT-4 for Medical Summarization. - Yash Mathur; Sanketh Rangreji; Raghav Kapoor; Medha Palavalli; Amanda Bertsch; Matthew Gormley 001 (ClinicalNLP@ACL, 2023)

To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing. - Sireesh Gururaja; Amanda Bertsch; Clara Na; David Gray Widder; Emma Strubell (EMNLP, 2023)

Prompt2Model: Generating Deployable Models from Natural Language Instructions. - Vijay Viswanathan 002; Chenyang Zhao; Amanda Bertsch; Tongshuang Wu; Graham Neubig (EMNLP (Demos), 2023)

Unlimiformer: Long-Range Transformers with Unlimited Length Input. - Amanda Bertsch; Uri Alon 002; Graham Neubig; Matthew Gormley 001 (NeurIPS, 2023)

LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs. - Tongshuang Wu; Haiyi Zhu; Maya Albayrak; Alexis Axon; Amanda Bertsch; Wenxing Deng; Ziqi Ding; Bill Guo; Sireesh Gururaja; Tzu-Sheng Kuo; Jenny T. Liang; Ryan Liu 001; et al. (CoRR, 2023)

It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk. - Amanda Bertsch; Alex Xie; Graham Neubig; Matthew R. Gormley (CoRR, 2023)

Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation. - Patrick Fernandes; Aman Madaan; Emmy Liu; António Farinhas; Pedro Henrique Martins; Amanda Bertsch; José G. C. de Souza; Shuyan Zhou; Tongshuang Wu; Graham Neubig; André F. T. Martins (Trans. Assoc. Comput. Linguistics, 2023)

A Taxonomy for Data Contamination in Large Language Models. - Medha Palavalli; Amanda Bertsch; Matthew Gormley 001 (CONDA, 2024)

In-Context Learning with Long-Context Models: An In-Depth Exploration. - Amanda Bertsch; Maor Ivgi; Uri Alon 002; Jonathan Berant; Matthew R. Gormley; Graham Neubig (CoRR, 2024)

From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models. - Sean Welleck; Amanda Bertsch; Matthew Finlayson; Hailey Schoelkopf; Alex Xie; Graham Neubig; Ilia Kulikov; Zaïd Harchaoui (Trans. Mach. Learn. Res., 2024)

Efficient Many-Shot In-Context Learning with Dynamic Block-Sparse Attention. - Emily Xiao; Chin-Jou Li; Yilin Zhang; Graham Neubig; Amanda Bertsch (ACL (1), 2025)

LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs. - Tongshuang Wu; Haiyi Zhu; Maya Albayrak; Alexis Axon; Amanda Bertsch; Wenxing Deng; Ziqi Ding; Boyuan Guo; Sireesh Gururaja; Tzu-Sheng Kuo; Jenny T. Liang; Ryan Liu 001; et al. (CHI Extended Abstracts, 2025)

Better Instruction-Following Through Minimum Bayes Risk. - Ian Wu; Patrick Fernandes; Amanda Bertsch; Seungone Kim; Sina Khoshfetrat Pakazad; Graham Neubig (ICLR, 2025)

In-Context Learning with Long-Context Models: An In-Depth Exploration. - Amanda Bertsch; Maor Ivgi; Emily Xiao; Uri Alon 002; Jonathan Berant; Matthew R. Gormley; Graham Neubig (NAACL (Long Papers), 2025)

Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions. - Emmy Liu; Amanda Bertsch; Lintang Sutawika; Lindia Tjuatja; Patrick Fernandes; Lara Marinov; Michael Chen; Shreya Singhal; Carolin Lawrence; Aditi Raghunathan; Kiril Gashteovski; Graham Neubig (CoRR, 2025)