A new paper published on arXiv questions the fundamental assumption that language models learn next-token prediction based solely on preceding text. The research argues that this next-token prediction is only conditionally correct, as real-world language generation is influenced by a multitude of non-textual factors like intentions, goals, and context. The paper proposes that for next-token prediction to be useful, the observed text must be a sufficient statistic for these latent circumstances, and introduces Retrieval Augmented Generation (RAG) and tool use as methods to achieve this conditional sufficiency. AI
Summary written by gemini-2.5-flash-lite from 1 sources. How we write summaries →
IMPACT Challenges the core assumption of next-token prediction in LLMs, suggesting current methods may overlook crucial contextual factors for true understanding.
RANK_REASON The cluster contains an academic paper discussing theoretical aspects of language model training. [lever_c_demoted from research: ic=1 ai=1.0]