Matheus Hrabowec Zambianco

Trapped by simplicity: When Transformers fail to learn from noisy features

Noise is ubiquitous in data used to train large language models, but it is not well understood whether these models are able to correctly generalize to inputs generated without noise. Here, we study noise-robust learning: are transformers trained on data with noisy features able to find a target fun…