23 citations · 56 across the 19 of their papers we have counts for
7 papers · 1 filter
To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing
Sireesh Gururaja, Amanda Bertsch, Clara Na +2
NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception. In this work, we seek to understand how to shape our future by…
It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk
Amanda Bertsch, Alex Xie, Graham Neubig +1
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 lo…
Prompt2Model: Generating Deployable Models from Natural Language Instructions
Vijay Viswanathan, Chenyang Zhao, Amanda Bertsch +2
Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provi…
LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs
Tongshuang Wu, Haiyi Zhu, Maya Albayrak +21
LLMs have shown promise in replicating human-like behavior in crowdsourcing tasks that were previously thought to be exclusive to human abilities. However, current efforts focus ma…
SummQA at MEDIQA-Chat 2023:In-Context Learning with GPT-4 for Medical Summarization
Yash Mathur, Sanketh Rangreji, Raghav Kapoor +3
Medical dialogue summarization is challenging due to the unstructured nature of medical conversations, the use of medical terminology in gold summaries, and the need to identify ke…
Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation
Patrick Fernandes, Aman Madaan, Emmy Liu +8
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data. However, this paradigm can lead to models that genera…
Unlimiformer: Long-Range Transformers with Unlimited Length Input
Amanda Bertsch, Uri Alon, Graham Neubig +1
Since the proposal of transformers, these models have been limited to bounded input lengths, because of their need to attend to every token in the input. In this work, we propose U…