批判语言模型研究的两大误区，倡导更开放的跨学科语言科学新范式。
You Can't Fight in Here! This is BBS!
- 驳斥统计模型=无语言能力的误解，强调大模型可提供语言科学洞见
- 反对'当前研究已到极限'的保守假设，主张持续深化探索
- 适合对语言学、认知科学与AI交叉研究感兴趣的学者
诺姆（语言理论家）与克劳黛特（计算语言科学家）就现代语言模型能否为语言科学提供重要启示展开对话。正当二人即将告别时，来自语言学、神经科学、认知科学、心理学、哲学和计算机科学的25位同行加入讨论。本文借此揭示两个核心问题：一是‘字符串统计拟态谬误’（认为语言模型因是统计模型，故无法具备语言能力或研究价值），二是‘够好即止假设’（认为2026年的语言模型研究已达语言科学所能提供的上限）。文章澄清了基于语言模型的研究在人类语言科学中的作用，并呼吁在人工智能时代建立更具包容性的语言科学研究体系，主动回应批评者关切，以推动人类语言与语言模型研究的双重进步。
原文摘要 · Abstract (English)
Norm, the formal theoretical linguist, and Claudette, the computational language scientist, have a lovely time discussing whether modern language models can inform important questions in the language sciences. Just as they are about to part ways until they meet again, 25 of their closest friends show up -- from linguistics, neuroscience, cognitive science, psychology, philosophy, and computer science. We use this discussion to highlight what we see as some common underlying issues: the String Statistics Strawman (the mistaken idea that LMs can't be linguistically competent or interesting because they, like their Markov model predecessors, are statistical models that learn from strings) and the As Good As it Gets Assumption (the idea that LM research as it stands in 2026 is the limit of what it can tell us about linguistics). We clarify the role of LM-based work for scientific insights into human language and advocate for a more expansive research program for the language sciences in the AI age, one that takes on the commentators' concerns in order to produce a better and more robust science of both human language and of LMs.
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