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Found 6 papers, 6 papers with code
Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision
We propose a Structure Refining Module, a simple yet effective first approach based on the similarities of the prompts by taking advantage of the intrinsic structure in the embedding space.
Alfred: A System for Prompted Weak Supervision
Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting.
Does CLIP Bind Concepts? Probing Compositionality in Large Image Models
In this work, we focus on the ability of a large pretrained vision and language model (CLIP) to encode compositional concepts and to bind variables in a structure-sensitive way (e. g., differentiating ''cube behind sphere'' from ''sphere behind cube'').
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning
We perform additional experiments to show that CSP improves generalization to higher-order attribute-attribute-object compositions (e. g., old white cat) and combinations of pretrained attributes and fine-tuned objects.
Learning from Multiple Noisy Partial Labelers
We evaluate our framework on three text classification and six object classification tasks.
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction
To demonstrate the effectiveness of DIAG-NRE, we apply it to two real-world datasets and present both significant and interpretable improvements over state-of-the-art methods.
