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Found 7 papers, 4 papers with code
Language Models in the Loop: Incorporating Prompting into Weak Supervision
Our experimental evaluation shows that prompting large language models within a weak supervision framework can provide significant gains in accuracy.
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
As our agent engages in conversation, it also estimates user satisfaction in its responses.
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications.
Training Complex Models with Multi-Task Weak Supervision
Snorkel MeTaL: A framework for training models with multi-task weak supervision
Ranked #1 on
Semantic Textual Similarity
on SentEval
Generating Titles for Web Tables
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.
Training Classifiers with Natural Language Explanations
Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification).
Fonduer: Knowledge Base Construction from Richly Formatted Data
We focus on knowledge base construction (KBC) from richly formatted data.
Databases
