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Found 4 papers, 2 papers with code
TorchQL: A Programming Framework for Integrity Constraints in Machine Learning
Finding errors in machine learning applications requires a thorough exploration of their behavior over data.
Do Machine Learning Models Learn Statistical Rules Inferred from Data?
Test-time adaptation reduces these violations by up to 68. 7% with relative performance improvement up to 32%.
Interactive Code Generation via Test-Driven User-Intent Formalization
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent.
CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation
Designing a suitable representation for code-reasoning tasks is challenging in aspects such as the kinds of program information to model, how to combine them, and how much context to consider.
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