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About this paper
Rahma Chaabouni, Eugene Kharitonov, Diane Bouchacourt, Emmanuel Dupoux, Marco Baroni
Languages spontaneously forming in deep multi-agent simulations can generalize to novel composite concepts when trained on large, varied input spaces. The research demonstrates that while compositionality is not a prerequisite for this generalization, it significantly improves the learnability and transmissibility of these emergent languages to new agents.
Key insights
- Neural networks can achieve compositional generalization in communication tasks when trained with sufficiently large and combinatorially varied input spaces, challenging prior assumptions of inherent limitations.
- Compositionality is not a necessary condition for generalization in emergent languages; agents can generalize using non-compositional (entangled) communication strategies.
- Compositionality serves as a strong advantage for language transmission, making emergent languages easier for new agents, including those with different architectures, to learn and acquire.
Summary by alphaXiv.
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