4 citations · 7 across the 6 of their papers we have counts for
6 papers
Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning
Mathieu Rita, Florian Strub, Rahma Chaabouni +3
While Reinforcement Learning (RL) has been proven essential for tuning large language models (LLMs), it can lead to reward over-optimization (ROO). Existing approaches address ROO…
Language Evolution with Deep Learning
Mathieu Rita, Paul Michel, Rahma Chaabouni +3
Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a s…
On the Correspondence between Compositionality and Imitation in Emergent Neural Communication
Emily Cheng, Mathieu Rita, Thierry Poibeau
Compositionality is a hallmark of human language that not only enables linguistic generalization, but also potentially facilitates acquisition. When simulating language emergence w…
Emergent Communication: Generalization and Overfitting in Lewis Games
Mathieu Rita, Corentin Tallec, Paul Michel +4
Lewis signaling games are a class of simple communication games for simulating the emergence of language. In these games, two agents must agree on a communication protocol in order…
On the role of population heterogeneity in emergent communication
Mathieu Rita, Florian Strub, Jean-Bastien Grill +2
Populations have often been perceived as a structuring component for language to emerge and evolve: the larger the population, the more structured the language. While this observat…
"LazImpa": Lazy and Impatient neural agents learn to communicate efficiently
Mathieu Rita, Rahma Chaabouni, Emmanuel Dupoux
Previous work has shown that artificial neural agents naturally develop surprisingly non-efficient codes. This is illustrated by the fact that in a referential game involving a spe…