Germàn Kruszewski | DeepAI

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∙ 06/30/2023

Should you marginalize over possible tokenizations?

Autoregressive language models (LMs) map token sequences to probabilitie...

0 Nadezhda Chirkova, et al. ∙

∙ 03/08/2023

disco: a toolkit for Distributional Control of Generative Models

Pre-trained language models and other generative models have revolutioni...

0 Germàn Kruszewski, et al. ∙

∙ 02/16/2023

Aligning Language Models with Preferences through f-divergence Minimization

Aligning language models with preferences can be posed as approximating ...

0 Dongyoung Go, et al. ∙

∙ 06/01/2022

On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting

The availability of large pre-trained models is changing the landscape o...

7 Tomasz Korbak, et al. ∙

∙ 12/10/2021

Sampling from Discrete Energy-Based Models with Quality/Efficiency Trade-offs

Energy-Based Models (EBMs) allow for extremely flexible specifications o...

0 Bryan Eikema, et al. ∙

∙ 12/01/2021

Controlling Conditional Language Models with Distributional Policy Gradients

Machine learning is shifting towards general-purpose pretrained generati...

0 Tomasz Korbak, et al. ∙

∙ 11/04/2021

Unsupervised and Distributional Detection of Machine-Generated Text

The power of natural language generation models has provoked a flurry of...

4 Matthias Gallé, et al. ∙

∙ 06/09/2021

Energy-Based Models for Code Generation under Compilability Constraints

Neural language models can be successfully trained on source code, leadi...

0 Tomasz Korbak, et al. ∙

∙ 03/15/2021

Emergence of self-reproducing metabolisms as recursive algorithms in an Artificial Chemistry

Researching the conditions for the emergence of life – not necessarily a...

0 Germàn Kruszewski, et al. ∙

∙ 04/07/2020

Class-Agnostic Continual Learning of Alternating Languages and Domains

Continual Learning has been often framed as the problem of training a mo...

0 Germàn Kruszewski, et al. ∙

∙ 03/17/2020

Combinatory Chemistry: Towards a Simple Model of Emergent Evolution

Researching the conditions for the emergence of life – not necessarily a...

0 Germàn Kruszewski, et al. ∙

∙ 03/18/2019

The emergence of number and syntax units in LSTM language models

Recent work has shown that LSTMs trained on a generic language modeling ...

0 Yair Lakretz, et al. ∙

∙ 02/25/2019

Cooperative Learning of Disjoint Syntax and Semantics

There has been considerable attention devoted to models that learn to jo...

6 Serhii Havrylov, et al. ∙

∙ 09/17/2018

The Fast and the Flexible: training neural networks to learn to follow instructions from small data

Learning to follow human instructions is a challenging task because whil...

0 Rezka Leonandya, et al. ∙

∙ 05/20/2018

Learning compositionally through attentive guidance

In this paper, we introduce Attentive Guidance (AG), a new mechanism to ...

0 Dieuwke Hupkes, et al. ∙

∙ 05/03/2018

What you can cram into a single vector: Probing sentence embeddings for linguistic properties

Although much effort has recently been devoted to training high-quality ...

0 Alexis Conneau, et al. ∙

∙ 02/18/2018

Memorize or generalize? Searching for a compositional RNN in a haystack

Neural networks are very powerful learning systems, but they do not read...

0 Adam Liska, et al. ∙

∙ 01/31/2017

CommAI: Evaluating the first steps towards a useful general AI

With machine learning successfully applied to new daunting problems almo...

0 Marco Baroni, et al. ∙

∙ 06/20/2016

The LAMBADA dataset: Word prediction requiring a broad discourse context

We introduce LAMBADA, a dataset to evaluate the capabilities of computat...

0 Denis Paperno, et al. ∙

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