1 code implementation • 2 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.
1 code implementation • 29 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.
1 code implementation • 20 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'').
1 code implementation • 7 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.
2 code implementations • 8 Jun 2021 • Peilin Yu, Tiffany Ding, Stephen H. Bach
We evaluate our framework on three text classification and six object classification tasks.
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.