28 papers
Nectar: Neural Estimation of Cached-Token Attention via Regression
João Monteiro, Michal Klein, Pierre Ablin +1
Evaluating softmax attention over a fixed long context requires reading every cached key-value pair for each new query token. For a given context (a book, a manual, a legal corpus)…
Amortized Maximum Inner Product Search with Learned Support Functions
Theo X. Olausson, João Monteiro, Michal Klein +1
Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of a vector taken within a database (the keys) that best aligns with a…
Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining
Thiziri Nait Saada, Louis Bethune, Michal Klein +3
Large-scale models are pretrained on massive web-crawled datasets containing documents of mixed quality, making data filtering essential. A popular method is Classifier-based Quali…
Learning Unmasking Policies for Diffusion Language Models
Metod Jazbec, Theo X. Olausson, Louis Béthune +6
Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient…
LaCy: What Small Language Models Can and Should Learn is Not Just a Question of Loss
Szilvia Ujváry, Louis Béthune, Pierre Ablin +3
Language models have consistently grown to compress more world knowledge into their parameters, but the knowledge that can be pretrained into them is upper-bounded by their paramet…
Locking Pretrained Weights via Deep Low-Rank Residual Distillation
Keitaro Sakamoto, Pierre Ablin, Federico Danieli +1
The quality of open-weight language models has dramatically improved in recent years. Sharing weights greatly facilitates model adoption by enabling their use across diverse hardwa…