Hugging Face Trending Papers
Dynamic Multi-Byte Prediction With Hierarchical Language Models
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arXiv AI
Aug 18
Dynamic Multi-Byte Prediction With Hierarchical Language Models
arXiv:2608. 15454v1 Announce Type: new Abstract: Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to their popular counterparts that use subword tokenization.
arXiv Machine Learning
Jun 29
EntMTP: Accelerating LLM Inference with Entropy Guided Multi Token Prediction
arXiv:2606. 27550v1 Announce Type: cross Abstract: Multi-token prediction has been shown to increase data density during training, improve downstream text-generation quality, and serves as the defacto approach for self-speculative decoding.
arXiv AI
Jul 21
Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage
arXiv:2601. 03043v4 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference efficiency.
arXiv Computation and Language
4d ago
When Tokenizers Fail: Byte-Level Chunking for Zero-Shot Transfer to Low-Resource Languages
arXiv:2608.27658v1 Announce Type: new Abstract: Subword tokenization hinders low-resource language processing by imposing frequency patterns from dominant languages onto script-sharing variants. Byte...
arXiv Machine Learning
Aug 24
STS: Efficient Sparse Attention with Speculative Token Sparsity
arXiv:2605.15508v3 Announce Type: replace Abstract: The quadratic complexity of attention imposes severe memory and computational bottlenecks on Large Language Model (LLM) inference. This challenge i...
arXiv Machine Learning
Aug 4
Structured Recurrent Mixers for Massively Parallelized Sequence Generation
arXiv:2605. 08696v4 Announce Type: replace-cross Abstract: Over the last two decades, language modeling has experienced a shift from the use of predominantly recurrent architectures that process tokens sequentially during training and inference to non-recurrent models that process sequence elements in parallel during training, which results in greater training efficiency and stability at the expense of lower inference throughput.