Long Context Language Models and their Biological Applications with Eric Nguyen - #690
Today, we're joined by Eric Nguyen, PhD student at Stanford University. In our conversation, we explore his research on long context foundation models and their application to biology particularly [Hyena] (https://hazyresearch.stanford.edu/blog/2023-03-07-hyena) , and its evolution into [Hyena DNA] (https://hazyresearch.stanford.edu/blog/2023-06-29-hyena-dna) and [Evo] (https://arcinstitute.org/news/blog/evo) models. We discuss Hyena, a convolutional-based language model developed to tackle the challenges posed by long context lengths in language modeling. We dig into the limitations of transformers in dealing with longer sequences, the motivation for using convolutional models over transformers, its model training and architecture, the role of FFT in computational optimizations, and model explainability in long-sequence convolutions. We also talked about Hyena DNA, a genomic foundation model pre-trained on 1 million tokens, designed to capture long-range dependencies in DNA sequences. Finally, Eric introduces Evo, a 7 billion parameter hybrid model integrating attention layers with Hyena DNA's convolutional framework. We cover generating and designing DNA with language models, hallucinations in DNA models, evaluation benchmarks, the trade-offs between state-of-the-art models, zero-shot versus a few-shot performance, and the exciting potential in areas like CRISPR-Cas gene editing.
The complete show notes for this episode can be found at [https://twimlai.com/go/690] (https://twimlai.com/go/690) .
25 Jun 2024
45 MINS
45:41
25 Jun 2024