5 papers · 1 filter
Compute Optimal Tokenization
Tomasz Limisiewicz, Artidoro Pagnoni, Srini Iyer +6
Scaling laws enable the optimal selection of data amount and language model size, yet the impact of the data unit, the token, on this relationship remains underexplored. In this wo…
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…
Byte Latent Transformer: Patches Scale Better Than Tokens
Artidoro Pagnoni, Ram Pasunuru, Pedro Rodriguez +11
We introduce the Byte Latent Transformer (BLT), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant imp…
In-context Pretraining: Language Modeling Beyond Document Boundaries
Weijia Shi, Sewon Min, Maria Lomeli +9
Large language models (LMs) are currently trained to predict tokens given document prefixes, enabling them to directly perform long-form generation and prompting-style tasks which…
RA-DIT: Retrieval-Augmented Dual Instruction Tuning
Xi Victoria Lin, Xilun Chen, Mingda Chen +9
Retrieval-augmented language models (RALMs) improve performance by accessing long-tail and up-to-date knowledge from external data stores, but are challenging to build. Existing ap…