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Found 6 papers, 3 papers with code
Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM
Data analysts have long sought to turn unstructured text data into meaningful concepts.
Clarify: Improving Model Robustness With Natural Language Corrections
Additionally, we use Clarify to find and rectify 31 novel hard subpopulations in the ImageNet dataset, improving minority-split accuracy from 21. 1% to 28. 7%.
Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows
LLM chains enable complex tasks by decomposing work into a sequence of sub-tasks.
Embedding Democratic Values into Social Media AIs via Societal Objective Functions
Finally, in Study 3, we replicate Study 1 using the democratic attitude model instead of manual labels to test its attitudinal and behavioral impact (N=558), and again find that the feed downranking using the societal objective function reduced partisan animosity (d=. 25).
Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design
Machine learning practitioners often end up tunneling on low-level technical details like model architectures and performance metrics.
Jury Learning: Integrating Dissenting Voices into Machine Learning Models
We introduce jury learning, a supervised ML approach that resolves these disagreements explicitly through the metaphor of a jury: defining which people or groups, in what proportion, determine the classifier's prediction.
