67 citations · 90 across the 3 of their papers we have counts for
5 papers
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models
Robert L. Logan, Ivana Balažević, Eric Wallace +3
Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. In this work, we show that finetuni…
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan +2
The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as fill-in-the-bl…
Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq
Qiang Ning, Hao Wu, Pradeep Dasigi +5
High-quality and large-scale data are key to success for AI systems. However, large-scale data annotation efforts are often confronted with a set of common challenges: (1) designin…
Active Bayesian Assessment for Black-Box Classifiers
Disi Ji, Robert L. Logan, Padhraic Smyth +1
Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications. In many such situations there is a critical nee…
Multimodal Attribute Extraction
Robert L. Logan, Samuel Humeau, Sameer Singh
The broad goal of information extraction is to derive structured information from unstructured data. However, most existing methods focus solely on text, ignoring other types of un…