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Found 5 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.
TopEx: Topic-based Explanations for Model Comparison
Meaningfully comparing language models is challenging with current explanation methods.
Rectifying Group Irregularities in Explanations for Distribution Shift
It is well-known that real-world changes constituting distribution shift adversely affect model performance.
Learning to Select Pivotal Samples for Meta Re-weighting
In this paper, we study how to learn to identify such a meta sample set from a large, imperfect training set, that is subsequently cleaned and used to optimize performance in the meta re-weighting setting.
Faithful Chain-of-Thought Reasoning
While Chain-of-Thought (CoT) prompting boosts Language Models' (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka.
