2 code implementations • 4 Apr 2024 • Yi Ren, Shangmin Guo, Linlu Qiu, Bailin Wang, Danica J. Sutherland
With the widespread adoption of Large Language Models (LLMs), the prevalence of iterative interactions among these models is anticipated to increase.
no code implementations • 7 Feb 2024 • Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Rame, Thomas Mesnard, Yao Zhao, Bilal Piot, Johan Ferret, Mathieu Blondel
Moreover, responses in these datasets are often sampled from a language model distinct from the one being aligned, and since the model evolves over training, the alignment phase is inevitably off-policy.
no code implementations • 5 Feb 2024 • Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, Mathieu Blondel
Aligning language models with human preferences is crucial for reducing errors and biases in these models.
no code implementations • 5 Feb 2024 • Samuel Garcin, James Doran, Shangmin Guo, Christopher G. Lucas, Stefano V. Albrecht
DRED generates levels using a generative model trained over an initial set of level parameters, reducing distributional shift, and achieves significant improvements in ZSG over adaptive level sampling strategies and UED methods.
no code implementations • 16 Jan 2024 • Shangmin Guo, Yi Ren, Stefano V. Albrecht, Kenny Smith
Although much research has been done on proposing new models or loss functions to improve the generalisation of artificial neural networks (ANNs), less attention has been directed to the impact of the training data on generalisation.
no code implementations • 5 Oct 2023 • Samuel Garcin, James Doran, Shangmin Guo, Christopher G. Lucas, Stefano V. Albrecht
A key limitation preventing the wider adoption of autonomous agents trained via deep reinforcement learning (RL) is their limited ability to generalise to new environments, even when these share similar characteristics with environments encountered during training.
no code implementations • 11 Feb 2023 • Yi Ren, Shangmin Guo, Wonho Bae, Danica J. Sutherland
We identify a significant trend in the effect of changes in this initial energy on the resulting features after fine-tuning.
3 code implementations • 2 Aug 2022 • Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho, Filippos Christianos, Mhairi Dunion, Elliot Fosong, Samuel Garcin, Shangmin Guo, Balint Gyevnar, Trevor McInroe, Georgios Papoudakis, Arrasy Rahman, Lukas Schäfer, Massimiliano Tamborski, Giuseppe Vecchio, Cheng Wang, Stefano V. Albrecht
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning.
1 code implementation • 15 Mar 2022 • Runfa Chen, Yu Rong, Shangmin Guo, Jiaqi Han, Fuchun Sun, Tingyang Xu, Wenbing Huang
After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potential in domain adaptive semantic segmentation.
Ranked #7 on Semantic Segmentation on SYNTHIA-to-Cityscapes
1 code implementation • ICLR 2022 • Yi Ren, Shangmin Guo, Danica J. Sutherland
Observing the learning path not only provides a new perspective for understanding knowledge distillation, overfitting, and learning dynamics, but also reveals that the supervisory signal of a teacher network can be very unstable near the best points in training on real tasks.
no code implementations • ICLR 2022 • Shangmin Guo, Yi Ren, Kory Wallace Mathewson, Simon Kirby, Stefano V Albrecht, Kenny Smith
Researchers are using deep learning models to explore the emergence of language in various language games, where simulated agents interact and develop an emergent language to solve a task.
1 code implementation • 7 Jun 2021 • Shangmin Guo, Yi Ren, Kory Mathewson, Simon Kirby, Stefano V. Albrecht, Kenny Smith
Researchers are using deep learning models to explore the emergence of language in various language games, where agents interact and develop an emergent language to solve tasks.
1 code implementation • 4 Dec 2020 • Shangmin Guo, Yi Ren, Agnieszka Słowik, Kory Mathewson
Referential games and reconstruction games are the most common game types for studying emergent languages.
1 code implementation • ICLR 2020 • Yi Ren, Shangmin Guo, Matthieu Labeau, Shay B. Cohen, Simon Kirby
The principle of compositionality, which enables natural language to represent complex concepts via a structured combination of simpler ones, allows us to convey an open-ended set of messages using a limited vocabulary.
1 code implementation • 4 Nov 2019 • Shangmin Guo
Although their encodeing method is not compositional like natural languages from a perspective of human beings, the emergent languages can be generalised to unseen inputs and, more importantly, are easier for models to learn.
no code implementations • 11 Oct 2019 • Shangmin Guo, Yi Ren, Serhii Havrylov, Stella Frank, Ivan Titov, Kenny Smith
Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics.
no code implementations • IJCNLP 2017 • Shangmin Guo, Kang Liu, Shizhu He, Cao Liu, Jun Zhao, Zhuoyu Wei
The IJCNLP-2017 Multi-choice Question Answering(MCQA) task aims at exploring the performance of current Question Answering(QA) techniques via the realworld complex questions collected from Chinese Senior High School Entrance Examination papers and CK12 website1.
1 code implementation • EACL 2017 • Shangmin Guo, Xiangrong Zeng, Shizhu He, Kang Liu, Jun Zhao
As one of the most important test of China, Gaokao is designed to be difficult enough to distinguish the excellent high school students.