2 citations · 2 across the 4 of their papers we have counts for
4 papers
Limits To (Machine) Learning
Zhimin Chen, Bryan Kelly, Semyon Malamud
Machine learning (ML) methods are highly flexible, but their ability to approximate the true data-generating process is fundamentally constrained by finite samples. We characterize…
Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders
Zhimin Chen, Chenyu Zhao, Ka Chun Mo +7
Modern large-scale recommendation systems rely heavily on user interaction history sequences to enhance the model performance. The advent of large language models and sequential mo…
Bilingual Adaptation of Monolingual Foundation Models
Gurpreet Gosal, Yishi Xu, Gokul Ramakrishnan +19
We present an efficient method for adapting a monolingual Large Language Model (LLM) to another language, addressing challenges of catastrophic forgetting and tokenizer limitations…
BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model
Nolan Dey, Daria Soboleva, Faisal Al-Khateeb +11
We introduce the Bittensor Language Model, called "BTLM-3B-8K", a new state-of-the-art 3 billion parameter open-source language model. BTLM-3B-8K was trained on 627B tokens from th…