34 citations · 72 across the 14 of their papers we have counts for
6 papers · 1 filter
Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
Yi Ren, Shangmin Guo, Linlu Qiu +2
With the widespread adoption of Large Language Models (LLMs), the prevalence of iterative interactions among these models is anticipated to increase. Notably, recent advancements i…
Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictability
Shangmin Guo, Yi Ren, Kory Mathewson +3
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. We…
Inductive Bias and Language Expressivity in Emergent Communication
Shangmin Guo, Yi Ren, Agnieszka Słowik +1
Referential games and reconstruction games are the most common game types for studying emergent languages. We investigate how the type of the language game affects the emergent lan…
Compositional Languages Emerge in a Neural Iterated Learning Model
Yi Ren, Shangmin Guo, Matthieu Labeau +2
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…
Emergence of Numeric Concepts in Multi-Agent Autonomous Communication
Shangmin Guo
With the rapid development of deep learning, most of current state-of-the-art techniques in natural langauge processing are based on deep learning models trained with argescaled st…
The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents
Shangmin Guo, Yi Ren, Serhii Havrylov +3
Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics. Recently, with the development of deep learning techniques, researc…