Invariant-Driven Structural Learning for Language-Modeling
Invariant-Driven Structural Learning for Language-Modeling
Exploration of an alternative approach to language modeling where learning is guided by structuralinvariants instead of minimizing loss in fixed architectures. We introduce a self-organizingprobabilistic automaton that can dynamically change its internal structure, including states,responsibilities, and routing, when constraints are violated.
Computational intelligence, Artificial intelligence, language modeling, probabilistic automata, structural learning, abstraction, non-neural language models, symbolic models, architecture learning, invariant-based learning, inductive bias, modularity
Computational intelligence, Artificial intelligence, language modeling, probabilistic automata, structural learning, abstraction, non-neural language models, symbolic models, architecture learning, invariant-based learning, inductive bias, modularity
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