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AI models learn semantic structure despite one-hot training, study finds

By PulseAugur Editorial · [3 sources] ·

A new research paper titled "Structure Before Collapse: Transient semantic geometry in next-token prediction" explores how language models learn semantic structure despite being trained with one-hot labels. The study identifies that while neural collapse theory predicts symmetric representations, language models develop latent structural features early in training. These emergent semantic geometries cluster by shared attributes but are transient, eventually leading to the predicted symmetric state. The research proposes a modification to existing models to better capture this emergent structure. AI

IMPACT This research offers insights into how language models develop semantic understanding, potentially guiding future model architectures and training methodologies.

RANK_REASON The cluster contains an academic paper detailing novel research findings on language model behavior.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

AI models learn semantic structure despite one-hot training, study finds

COVERAGE [3]

  1. arXiv cs.CL TIER_1 English(EN) · Yize Zhao, Isabel Papadimitriou, Christos Thrampoulidis ·

    Structure Before Collapse: Transient semantic geometry in next-token prediction

    arXiv:2606.26749v1 Announce Type: cross Abstract: Neural Collapse predicts that balanced one-hot classification pushes model representations to be equally far from each other; a symmetric configuration that depends only on the output label and ignores any semantic similarity in t…

  2. arXiv cs.LG TIER_1 English(EN) · Christos Thrampoulidis ·

    Structure Before Collapse: Transient semantic geometry in next-token prediction

    Neural Collapse predicts that balanced one-hot classification pushes model representations to be equally far from each other; a symmetric configuration that depends only on the output label and ignores any semantic similarity in the inputs. This creates a puzzle: next-token predi…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Structure Before Collapse: Transient semantic geometry in next-token prediction

    Neural Collapse predicts that balanced one-hot classification pushes model representations to be equally far from each other; a symmetric configuration that depends only on the output label and ignores any semantic similarity in the inputs. This creates a puzzle: next-token predi…

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