arXiv:2606.27228v1 Announce Type: new Abstract: Formal semantics has shown that sentence meanings arise by recursively composing lexical meanings, yet much of the literature on semantic universals models either lexicons with fixed signal structures or holistic composition without interpretable lexical parts. We introduce a framework that integrates this fundamental insight of formal semantics in evolutionary modeling, by allowing lexical meanings and a composition function to co-evolve under pressures for conceptual simplicity and communicative accuracy. We apply this framework to the evolutio
This paper introduces a new framework for modeling language evolution, integrating formal semantics with evolutionary pressures, which builds upon decades of linguistic research and computational modeling advancements.
A strategic reader should care as a deeper understanding of language evolution, particularly compositionality, is crucial for advancing AI's ability to understand and generate human-like language, impacting AI agent development.
The theoretical understanding of how language's complex structure, specifically its compositional nature, could have evolved is refined, offering new avenues for developing more sophisticated language models.
- · AI researchers
- · Linguists
- · Natural Language Processing (NLP) sector
- · Models relying on fixed signal structures
- · Purely holistic compositional models
The publication provides a new theoretical model for understanding the co-evolution of lexical meanings and composition functions in language.
This improved theoretical foundation could lead to the development of more biologically plausible and human-like AI language models.
Enhanced AI language understanding could accelerate the capabilities of AI agents, making them more effective in complex tasks and interactions.
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