5 papers
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…
Fast Byte Latent Transformer
Julie Kallini, Artidoro Pagnoni, Tomasz Limisiewicz +5
Recent byte-level language models (LMs) match the performance of token-level models without relying on subword vocabularies, yet their utility is limited by slow, byte-by-byte auto…
Latent Speech-Text Transformer
Yen-Ju Lu, Yashesh Gaur, Wei Zhou +8
Auto-regressive speech-text models pre-trained on interleaved text tokens and discretized speech tokens demonstrate strong speech understanding and generation, yet remain substanti…
Memory Layers at Scale
Vincent-Pierre Berges, Barlas OÄuz, Daniel Haziza +3
Memory layers use a trainable key-value lookup mechanism to add extra parameters to a model without increasing FLOPs. Conceptually, sparsely activated memory layers complement comp…
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…