31 citations · 101 across the 22 of their papers we have counts for
25 papers
Should you marginalize over possible tokenizations?
Nadezhda Chirkova, Germán Kruszewski, Jos Rozen +1
Autoregressive language models (LMs) map token sequences to probabilities. The usual practice for computing the probability of any character string (e.g. English sentences) is to f…
disco: a toolkit for Distributional Control of Generative Models
Germán Kruszewski, Jos Rozen, Marc Dymetman
Pre-trained language models and other generative models have revolutionized NLP and beyond. However, these models tend to reproduce undesirable biases present in their training dat…
Aligning Language Models with Preferences through f-divergence Minimization
Dongyoung Go, Tomasz Korbak, Germán Kruszewski +3
Aligning language models with preferences can be posed as approximating a target distribution representing some desired behavior. Existing approaches differ both in the functional…
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting
Tomasz Korbak, Hady Elsahar, Germán Kruszewski +1
The availability of large pre-trained models is changing the landscape of Machine Learning research and practice, moving from a training-from-scratch to a fine-tuning paradigm. Whi…
Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offs
Bryan Eikema, Germán Kruszewski, Hady Elsahar +1
Energy-Based Models (EBMs) allow for extremely flexible specifications of probability distributions. However, they do not provide a mechanism for obtaining exact samples from these…
Controlling Conditional Language Models without Catastrophic Forgetting
Tomasz Korbak, Hady Elsahar, German Kruszewski +1
Machine learning is shifting towards general-purpose pretrained generative models, trained in a self-supervised manner on large amounts of data, which can then be applied to solve…