Papers with byte-level models
ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models (2022.tacl-1)
Copied to clipboard
Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel
| Challenge: | a number of pre-trained language models use sequences of tokens corresponding to word units . token-free models that operate directly on raw text have many advantages . |
| Approach: | They propose a standard Transformer architecture that can be used to process byte sequences . they also characterize trade-offs in terms of parameter count, training FLOPs, and inference speed . |
| Outcome: | The proposed model is more robust to noise and more robust on spelling and pronunciation tasks. |
MANTa: Efficient Gradient-Based Tokenization for End-to-End Robust Language Modeling (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Subword tokenization algorithms have been an essential component of language modeling but their static nature results in important flaws that degrade the models’ downstream performance and robustness. |
| Approach: | They propose a module for Adaptive Neural TokenizAtion that is differentiable and trained end-to-end with the language model. |
| Outcome: | The proposed tokenizer improves robustness to character perturbations and out-of-domain data. |
Byte Latent Transformer: Patches Scale Better Than Tokens (2025.acl-long)
Copied to clipboard
Artidoro Pagnoni, Ramakanth Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason E Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, Srini Iyer
| Challenge: | Existing large language models (LLMs) are trained on bytes, except for tokenization, which groups bytes into a static set of tokens. |
| Approach: | They propose a new byte-level LLM architecture that encodes bytes into dynamically sized patches, which serve as the primary units of computation. |
| Outcome: | The proposed architecture matches tokenization-based models with improvements in inference efficiency and robustness. |