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Paper questions usefulness of next-token prediction in LLMs

By PulseAugur Editorial · [2 sources] ·

A new paper published on arXiv explores the limitations of next-token prediction in language models. It argues that current models, trained on observed sequences, do not fully capture the conditional laws of language generation because they miss non-textual circumstances like intentions and context. The research suggests that for next-token prediction to be truly useful, the observed text must be a sufficient statistic for these latent circumstances, a condition often not met by heterogeneous training corpora. AI

IMPACT This paper challenges fundamental assumptions in LLM training, suggesting a need for new approaches beyond simple next-token prediction to achieve true contextual understanding.

RANK_REASON The cluster contains an academic paper discussing theoretical limitations of language model training.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Francesco Corielli ·

    When Is Next-Token Prediction Useful? Marginalization, Ergodicity, Mixture Identifiability, Local Sufficiency, RAG, Tools, and Programming

    arXiv:2605.23278v1 Announce Type: cross Abstract: Language models trained on observed sequences are often described as learning the conditional distribution of the next token given previous tokens. This description is only conditionally correct. A model trained on realized token …

  2. arXiv stat.ML TIER_1 English(EN) · Francesco Corielli ·

    When Is Next-Token Prediction Useful? Marginalization, Ergodicity, Mixture Identifiability, Local Sufficiency, RAG, Tools, and Programming

    Language models trained on observed sequences are often described as learning the conditional distribution of the next token given previous tokens. This description is only conditionally correct. A model trained on realized token trajectories does not observe full conditional law…

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