Punya Syon Pandey

Undesirable training data can lead to undesirable model output. This dynamic is commonly phrased as "garbage in, garbage out" and it is a key issue for frontier models trained on web-scale data. How can we efficiently identify these bad apples in massive training datasets (with trillions of tokens)? Influence functions...

TL;DR This post discusses our explorations into the effects of domain-specific fine-tuning and how the characteristics of fine-tuning data relate to adversarial vulnerability. We also explore its implications for real-world applications, and offer insights into the importance of dataset engineering as an approach toward achieving true alignment in AI systems....