The advent of pre-trained generative models, particularly large language models, has revolutionized the field of NLP and beyond. However, these models tend to reproduce the biases present in their training data. Also, they may overlook important features of the data that are challenging to capture, such as guaranteeing the compilability of generated code even when trained on clean code. To address these limitations, researchers have introduced distributional control techniques. These techniques, not limited to language, allow controlling the prevalence (i.e. expectations) of any features of interest in the model’s generated outputs. Despite their potential, the widespread adoption of these techniques has been hindered by the difficulty in adapting the existing complex, disconnected code. Here, we present disco, a Python toolkit that brings these techniques to the wider public. We release disco as an open-source library.
disco: a toolkit for DIStributional COntrol of generative models
| German Kruszewski, Jos Rozen, Marc Dymetman |
| 61st Annual Meeting of the Association for Computational Linguistics (ACL), Toronto, Canada, 9-14 July, 2023 |
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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