Finetuning language models with reinforcement learning (RL), eg from human feedback
(HF), is a prominent method for alignment. But optimizing against a reward model can …

Emergent communication, or emergent language, is the field of research which studies how
human language-like communication systems emerge de novo in deep multi-agent …

The field of emergent language represents a novel area of research within the domain of
artificial intelligence, particularly within the context of multi-agent reinforcement learning …

Natural language has the universal properties of being compositional and grounded in
reality. The emergence of linguistic properties is often investigated through simulations of …

In language emergence, neural agents acquire communication skills by interacting with one
another and the environment. Through these interactions, agents learn to connect or ground …

We formulate and test a technique to use Emergent Communication (EC) with a pre-trained
multilingual model to improve on modern Unsupervised NMT systems, especially for low …

Humans have clear cross-modal preferences when matching certain novel words to visual
shapes. Evidence suggests that these preferences play a prominent role in our linguistic …

While self-supervised learning (SSL) in speech has greatly reduced the reliance of speech
processing systems on annotated corpora, the success of SSL still hinges on the availability …

The rise of large language models as the workhorse of NLP, and the continuous release of
better models (OpenAI, 2023; Pichai, 2023; Schulman et al., 2022, inter alia) has created a …

Deep neural network based speech processing systems have found widespread
applications in daily life, being employed for tasks such as automatic speech recognition …