no code implementations • 4 May 2022 • Ryan Smith, Jason A. Fries, Braden Hancock, Stephen H. Bach
Our experimental evaluation shows that prompting large language models within a weak supervision framework can provide significant gains in accuracy.
1 code implementation • ACL 2019 • Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazaré, Jason Weston
As our agent engages in conversation, it also estimates user satisfaction in its responses.
no code implementations • 2 Dec 2018 • Stephen H. Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alexander Ratner, Braden Hancock, Houman Alborzi, Rahul Kuchhal, Christopher Ré, Rob Malkin
Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications.
1 code implementation • 5 Oct 2018 • Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, Christopher Ré
Snorkel MeTaL: A framework for training models with multi-task weak supervision
Ranked #1 on Semantic Textual Similarity on SentEval
no code implementations • 30 Jun 2018 • Braden Hancock, Hongrae Lee, Cong Yu
This is accomplished by extracting many text snippets that have potentially relevant information to the table, encoding them into an input sequence, and using both copy and generation mechanisms in the decoder to balance relevance and readability of the generated title.
2 code implementations • ACL 2018 • Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, Christopher Ré
Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification).
1 code implementation • 15 Mar 2017 • Sen Wu, Luke Hsiao, Xiao Cheng, Braden Hancock, Theodoros Rekatsinas, Philip Levis, Christopher Ré
We focus on knowledge base construction (KBC) from richly formatted data.