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Diana Mincu

2 accepted papers

2024

Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering

ICLR 2024poster

Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs). However, LLMs suffer from prompt brittleness and various bias factors in the prompt, including but not limited to the formatting, the choice verbalizers, and the ICL examples. To addres…

Cited by 51SourcePDFScholar
2022

Diagnosing failures of fairness transfer across distribution shift in real-world medical settings

NeurIPS 2022accept

Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings. Importantly, the success of any mitigation strategy strongly depends on the \textit{structure} of the shift. Despite this, there h…

Cited by 74SourcePDFScholar