no code implementations • NAACL (GeBNLP) 2022 • Amanda Bertsch, Ashley Oh, Sanika Natu, Swetha Gangu, Alan W. black, Emma Strubell
We extend our analysis to a longitudinal study of bias in film dialogue over the last 110 years and find that continued pre-training on OpenSubtitles encodes additional bias into BERT.
1 code implementation • WNUT (ACL) 2021 • Amanda Bertsch, Steven Bethard
On Wikipedia, an online crowdsourced encyclopedia, volunteers enforce the encyclopedia’s editorial policies.
no code implementations • 30 Apr 2024 • Amanda Bertsch, Maor Ivgi, Uri Alon, Jonathan Berant, Matthew R. Gormley, Graham Neubig
As model context lengths continue to increase, the number of demonstrations that can be provided in-context approaches the size of entire training datasets.
no code implementations • 11 Oct 2023 • Sireesh Gururaja, Amanda Bertsch, Clara Na, David Gray Widder, Emma Strubell
NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception.
1 code implementation • 2 Oct 2023 • Amanda Bertsch, Alex Xie, Graham Neubig, Matthew R. Gormley
Minimum Bayes Risk (MBR) decoding is a method for choosing the outputs of a machine learning system based not on the output with the highest probability, but the output with the lowest risk (expected error) among multiple candidates.
1 code implementation • 23 Aug 2023 • Vijay Viswanathan, Chenyang Zhao, Amanda Bertsch, Tongshuang Wu, Graham Neubig
In this paper, we propose Prompt2Model, a general-purpose method that takes a natural language task description like the prompts provided to LLMs, and uses it to train a special-purpose model that is conducive to deployment.
no code implementations • 19 Jul 2023 • 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, Ihita Mandal, Jeremiah Milbauer, Xiaolin Ni, Namrata Padmanabhan, Subhashini Ramkumar, Alexis Sudjianto, Jordan Taylor, Ying-Jui Tseng, Patricia Vaidos, Zhijin Wu, Wei Wu, Chenyang Yang
We reflect on human and LLMs' different sensitivities to instructions, stress the importance of enabling human-facing safeguards for LLMs, and discuss the potential of training humans and LLMs with complementary skill sets.
1 code implementation • 30 Jun 2023 • Yash Mathur, Sanketh Rangreji, Raghav Kapoor, Medha Palavalli, Amanda Bertsch, Matthew R. Gormley
For full-note summarization (Task B), we use a similar solution with k=1.
1 code implementation • NeurIPS 2023 • Amanda Bertsch, Uri Alon, Graham Neubig, Matthew R. Gormley
This kNN index can be kept on either the GPU or CPU memory and queried in sub-linear time; this way, we can index practically unlimited input sequences, while every attention head in every decoder layer retrieves its top-k keys, instead of attending to every key.
no code implementations • 1 May 2023 • 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
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data.
1 code implementation • 27 Oct 2022 • Amanda Bertsch, Graham Neubig, Matthew R. Gormley
As a sample application, we demonstrate that applying perspective shifting to a dialogue summarization dataset (SAMSum) substantially improves the zero-shot performance of extractive news summarization models on this data.