Search Results for author: Peilin Yu

Found 6 papers, 6 papers with code

Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision

1 code implementation2 Feb 2024 Jinyan Su, Peilin Yu, Jieyu Zhang, Stephen H. Bach

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

1 code implementation29 May 2023 Peilin Yu, Stephen Bach

Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting.

Spam detection

Does CLIP Bind Concepts? Probing Compositionality in Large Image Models

1 code implementation20 Dec 2022 Martha Lewis, Nihal V. Nayak, Peilin Yu, Qinan Yu, Jack Merullo, Stephen H. Bach, Ellie Pavlick

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'').

Language Modelling Open-Ended Question Answering

Learning to Compose Soft Prompts for Compositional Zero-Shot Learning

1 code implementation7 Apr 2022 Nihal V. Nayak, Peilin Yu, Stephen H. Bach

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.

Attribute Compositional Zero-Shot Learning +1

Learning from Multiple Noisy Partial Labelers

2 code implementations8 Jun 2021 Peilin Yu, Tiffany Ding, Stephen H. Bach

We evaluate our framework on three text classification and six object classification tasks.

Attribute text-classification +2

DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction

1 code implementation ACL 2019 Shun Zheng, Xu Han, Yankai Lin, Peilin Yu, Lu Chen, Ling Huang, Zhiyuan Liu, Wei Xu

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.

Relation Relation Extraction

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