In contrast, in non-private settings, practitioners commonly utilize "adaptive" hyperparameter optimization methods such as Gaussian process-based optimization, which select the next candidate based on information gathered from previous outputs. This substantial contrast between private and non-private hyperparameter optimization underscores a critical concern. In our paper, we introduce DP-HyPO, a pioneering framework for "adaptive"

How to evaluate Large Language Models (LLMs) in code generation remains an open question.

Most applications of machine learning to classification assume a closed set of balanced classes.

This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.