89 citations · 196 across the 6 of their papers we have counts for
7 papers · 1 filter
Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models
Weixin Liang, Lili Yu, Liang Luo +8
The development of large language models (LLMs) has expanded to multi-modal systems capable of processing text, images, and speech within a unified framework. Training these models…
Text Quality-Based Pruning for Efficient Training of Language Models
Vasu Sharma, Karthik Padthe, Newsha Ardalani +8
In recent times training Language Models (LMs) have relied on computationally heavy training over massive datasets which makes this training process extremely laborious. In this pa…
LIMA: Less Is More for Alignment
Chunting Zhou, Pengfei Liu, Puxin Xu +12
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and re…
CM3: A Causal Masked Multimodal Model of the Internet
Armen Aghajanyan, Bernie Huang, Candace Ross +8
We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents that can contain both text and image tokens. Our new…
HTLM: Hyper-Text Pre-Training and Prompting of Language Models
Armen Aghajanyan, Dmytro Okhonko, Mike Lewis +4
We introduce HTLM, a hyper-text language model trained on a large-scale web crawl. Modeling hyper-text has a number of advantages: (1) it is easily gathered at scale, (2) it provid…
Multi-task Retrieval for Knowledge-Intensive Tasks
Jean Maillard, Vladimir Karpukhin, Fabio Petroni +4
Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditi…