Search Results for author:
Found 12 papers, 8 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.
Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows
LLM chains enable complex tasks by decomposing work into a sequence of sub-tasks.
Mosaic: An Architecture for Scalable & Interoperable Data Views
Mosaic is an architecture for greater scalability, extensibility, and interoperability of interactive data views.
rTisane: Externalizing conceptual models for data analysis increases engagement with domain knowledge and improves statistical model quality
We leverage these findings to develop rTisane, a DSL for expressing conceptual models augmented with an interactive disambiguation process.
ScatterShot: Interactive In-context Example Curation for Text Transformation
ScatterShot iteratively slices unlabeled data into task-specific patterns, samples informative inputs from underexplored or not-yet-saturated slices in an active learning manner, and helps users label more efficiently with the help of an LLM and the current example set.
Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships
Proper statistical modeling incorporates domain theory about how concepts relate and details of how data were measured.
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions.
CORAL: COde RepresentAtion Learning with Weakly-Supervised Transformers for Analyzing Data Analysis
Large scale analysis of source code, and in particular scientific source code, holds the promise of better understanding the data science process, identifying analytical best practices, and providing insights to the builders of scientific toolkits.
Boba: Authoring and Visualizing Multiverse Analyses
Multiverse analysis is an approach to data analysis in which all "reasonable" analytic decisions are evaluated in parallel and interpreted collectively, in order to foster robustness and transparency.
Human-Computer Interaction
Errudite: Scalable, Reproducible, and Testable Error Analysis
Though error analysis is crucial to understanding and improving NLP models, the common practice of manual, subjective categorization of a small sample of errors can yield biased and incomplete conclusions.
