7.8k citations · 9.1k across the 24 of their papers we have counts for
28 papers
TLPO: Token-Level Policy Optimization for Mitigating Language Confusion in Large Language Models
Jinho Choo, JunSeung Lee, Jimyeong Kim +3
Large language models (LLMs) demonstrate strong multilingual capabilities, yet often fail to consistently generate responses in the intended language, exhibiting a phenomenon known…
Partition Tree Weighting for Non-Stationary Stochastic Bandits
Joel Veness, Marcus Hutter, Andras Gyorgy +1
This paper considers a generalisation of universal source coding for interaction data, namely data streams that have actions interleaved with observations. Our goal will be to cons…
Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data
David Heurtel-Depeiges, Anian Ruoss, Joel Veness +1
Foundation models are strong data compressors, but when accounting for their parameter size, their compression ratios are inferior to standard compression algorithms. Naively reduc…
Learning Universal Predictors
Jordi Grau-Moya, Tim Genewein, Marcus Hutter +8
Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data. Broad exposure to different tasks leads to versatile represe…
Language Modeling Is Compression
Grégoire Delétang, Anian Ruoss, Paul-Ambroise Duquenne +9
It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has f…
Randomized Positional Encodings Boost Length Generalization of Transformers
Anian Ruoss, Grégoire Delétang, Tim Genewein +5
Transformers have impressive generalization capabilities on tasks with a fixed context length. However, they fail to generalize to sequences of arbitrary length, even for seemingly…