Projected Language Models: A Large Model Pre-Segmented Into Smaller Ones
This paper has been accepted at the Foundation Models in the Wild workshop at ICML 2024. Large language models are versatile tools but are not suitable for small inference budgets. Small models have more efficient inference but their lower capacity means that their performance can be good only if one limits their scope to a specialized domain. This paper explores how to get a small language model with good specialized accuracy, even when specialization data is unknown during pretraining. We propose a novel architecture, projected networks (PN). PN is a high capacity network whose parameters…
Including memory banks in a natural language processing architecture increases model capacity by equipping it with additional data at inference time. In this paper, we build upon kNN-LM, which uses a pre-trained language model together with an exhaustive kNN search through the training data (memory bank) to achieve state-of-the-art results.…
This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP) workshop at NeurIPS 2024. Speculative decoding is a prominent technique to speed up the inference of a large target language model based on predictions of an auxiliary draft model. While effective, in application-specific settings, it often involves…
This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP) Workshop at NeurIPS 2024. The pre-training phase of language models often begins with randomly initialized parameters. With the current trends in scaling models, training their large number of parameters can be extremely slow and costly. In contrast,…