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.

BACON allows computational resources to be focused on simulations that provide the most value in terms of reducing epistemic uncertainty.

A Partially Observable Markov Decision Process (POMDP) is a general framework for the above problem.

While numerous forecasters have been proposed using different network architectures, the Transformer-based models have state-of-the-art performance in time series forecasting.