4 research outputs found
Towards More Human-like AI Communication: A Review of Emergent Communication Research
In the recent shift towards human-centric AI, the need for machines to
accurately use natural language has become increasingly important. While a
common approach to achieve this is to train large language models, this method
presents a form of learning misalignment where the model may not capture the
underlying structure and reasoning humans employ in using natural language,
potentially leading to unexpected or unreliable behavior. Emergent
communication (Emecom) is a field of research that has seen a growing number of
publications in recent years, aiming to develop artificial agents capable of
using natural language in a way that goes beyond simple discriminative tasks
and can effectively communicate and learn new concepts. In this review, we
present Emecom under two aspects. Firstly, we delineate all the common
proprieties we find across the literature and how they relate to human
interactions. Secondly, we identify two subcategories and highlight their
characteristics and open challenges. We encourage researchers to work together
by demonstrating that different methods can be viewed as diverse solutions to a
common problem and emphasize the importance of including diverse perspectives
and expertise in the field. We believe a deeper understanding of human
communication is crucial to developing machines that can accurately use natural
language in human-machine interactions.Comment: 25 pages, 9 figures, 2 table
On the role of population heterogeneity in emergent communication
International audiencePopulations 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 observation is widespread in the sociolinguistic literature, it has not been consistently reproduced in computer simulations with neural agents. In this paper, we thus aim to clarify this apparent contradiction. We explore emergent language properties by varying agent population size in the speaker-listener Lewis Game. After reproducing the experimental difference, we challenge the simulation assumption that the agent community is homogeneous. We then investigate how speaker-listener asymmetry alters language structure through the analysis a potential diversity factor: learning speed. From then, we leverage this observation to control population heterogeneity without introducing confounding factors. We finally show that introducing such training speed heterogeneities naturally sort out the initial contradiction: larger simulated communities start developing more stable and structured languages