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A Precis of Language Models are not Models of Language

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arxiv 2205.07634 v1 pith:FYUZPA6Z submitted 2022-05-16 cs.CL

classification cs.CL
keywords languagemodelsneuralartificialnaturalapplicationareascognition
verification ladder T0 review T1 audit T2 compute T3 formal
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Natural Language Processing is one of the leading application areas in the current resurgence of Artificial Intelligence, spearheaded by Artificial Neural Networks. We show that despite their many successes at performing linguistic tasks, Large Neural Language Models are ill-suited as comprehensive models of natural language. The wider implication is that, in spite of the often overbearing optimism about AI, modern neural models do not represent a revolution in our understanding of cognition.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-granular Training Strategies for Robust Multi-hop Reasoning Over Noisy and Heterogeneous Knowledge Sources

    cs.CL 2025-02 reject novelty 2.0 of 10

    AMKOR is described as a state-of-the-art multi-hop QA system, but the paper provides no reproducible evidence and the reported numbers appear unverifiable.

  2. Generalization of Medical Large Language Models through Cross-Domain Weak Supervision

    cs.CL 2025-02 reject novelty 2.0 of 10

    A claimed curriculum-based fine-tuning framework for medical LLMs reports better question answering and response generation, but lacks reproducible evidence.

  3. Instruction Tuning for Story Understanding and Generation with Weak Supervision

    cs.CL 2025-01 reject novelty 2.0 of 10

    The paper claims a weak-to-strong instruction tuning curriculum improves story generation, but the method is standard sequential fine-tuning and the reported results are not reproducible.

  4. Cross-Cultural Fashion Design via Interactive Large Language Models and Diffusion Models

    cs.CL 2025-01 reject novelty 2.0 of 10

    The authors claim that LLM prompt refinement plus a CLIP-based weak supervision filter improves diffusion-based fashion image generation, but the evidence is unverifiable and internally inconsistent.

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