168 citations · 795 across the 28 of their papers we have counts for
4 papers · 1 filter
Textbooks Are All You Need II: phi-1.5 technical report
Yuanzhi Li, Sébastien Bubeck, Ronen Eldan +3
We continue the investigation into the power of smaller Transformer-based language models as initiated by \textbf{TinyStories} -- a 10 million parameter model that can produce cohe…
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
Mert Yuksekgonul, Varun Chandrasekaran, Erik Jones +5
We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text. We propose modeling factual queries as constrain…
Textbooks Are All You Need
Suriya Gunasekar, Yi Zhang, Jyoti Aneja +16
We introduce phi-1, a new large language model for code, with significantly smaller size than competing models: phi-1 is a Transformer-based model with 1.3B parameters, trained for…
(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability
Mathieu Even, Scott Pesme, Suriya Gunasekar +1
In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over dia…