Papers by Peilin Yu
Alfred: A System for Prompted Weak Supervision (2023.acl-demo)
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| Challenge: | Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting. |
| Approach: | They propose to use Python to create training data by prompting for machine learning . they find that it improves query throughput by 2.9x versus a naive approach . |
| Outcome: | The proposed system improves query throughput by 2.9x versus a naive approach. |
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction (P19-1)
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| Challenge: | Existing methods for labeling relational facts require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly. |
| Approach: | They propose a neural pattern diagnosis framework that can summarize and refine relation-specific patterns with human experts in the loop. |
| Outcome: | The proposed framework can summarize and refine high-quality relational patterns from noise data with human experts in the loop. |
Gardener: An Agentic AI System for Single-Cell RNA Sequence Analysis (2026.acl-demo)
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| Challenge: | Existing large language models encode workflow progress as conversational state and rely on cloud-centric execution, which hinders traceability and auditability. |
| Approach: | Gardener is an open-source desktop application for macOS and windows under the Apache License 2.0. |
| Outcome: | Gardener is released as an open-source desktop application for macOS and Windows under the Apache License 2.0. |
Does CLIP Bind Concepts? Probing Compositionality in Large Image Models (2024.findings-eacl)
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| Challenge: | Large-scale neural network models combining text and images have made incredible progress in recent years, but to what extent they encode compositional representations of the concepts over which they operate remains an open question . |
| Approach: | They compare the performance of a large pretrained vision and language model (CLIP) to a set of three synthetic datasets designed to test concept binding. |
| Outcome: | The proposed model can encode compositional concepts and bind variables in a structure-sensitive way, e.g., differentiating ‘cube behind sphere’ from ‘cub behind cube’. |