NoRa (Noisy Rationales) is a dataset specifically designed to evaluate the reasoning capabilities of Large Language Models (LLMs) when faced with noisy reasoning processes. This dataset contains reasoning tasks with both clean reasoning samples and samples with different types and difficulties of noise.
Math-9: Mathematical operations in… See the full description on the dataset page:
https://huggingface.co/datasets/GazeEzio/NoRa_Test.
