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Found 2 papers, 1 papers with code
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"
Data scientists develop ML pipelines in an iterative manner: they repeatedly screen a pipeline for potential issues, debug it, and then revise and improve its code according to their findings.
Improving Retrieval-Augmented Large Language Models via Data Importance Learning
There are exponentially many terms in the multilinear extension, and one key contribution of this paper is a polynomial time algorithm that computes exactly, given a retrieval-augmented model with an additive utility function and a validation set, the data importance of data points in the retrieval corpus using the multilinear extension of the model's utility function.
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