LLMs exhibit three geometric phases in next-token prediction—seeding multiplexing, hoisting overriding, and focal convergence—where predictive subspaces rise, stabilize, and converge across layers.
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A topic-modeling framework measures document-level thematic consistency in translations by aligning key tokens across languages with a bilingual dictionary and scoring via cosine similarity, providing explainable insights beyond sentence-level metrics.
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A Geometric Perspective on Next-Token Prediction in Large Language Models: Three Emerging Phases
LLMs exhibit three geometric phases in next-token prediction—seeding multiplexing, hoisting overriding, and focal convergence—where predictive subspaces rise, stabilize, and converge across layers.
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An Explainable Approach to Document-level Translation Evaluation with Topic Modeling
A topic-modeling framework measures document-level thematic consistency in translations by aligning key tokens across languages with a bilingual dictionary and scoring via cosine similarity, providing explainable insights beyond sentence-level metrics.