大语言模型与不可能的语言习得："虚假承诺"或对当前人工智能观点的颠覆
Large Language Models and Impossible Language Acquisition: "False Promise" or an Overturn of our Current Perspective towards AI
AI总结：
本文通过实验探讨大语言模型学习可能与不可能语言的能力，发现GPT-2模型在自然语言任务上表现优于不可能语言任务，而LSTM模型则无显著差异，挑战了Chomsky对AI的理性主义基础观点。
摘要：
在Chomsky的挑衅性批评《CHATGPT的虚假承诺》中，大语言模型（LLMs）被描述为仅能预测模式的工具，无法像人类一样通过内在因果和自我修正结构习得语言，因此无法区分可能与不可能的语言。它代表了对AI智力基础的根本挑战，因为它整合了LLMs方法论中的主要问题，并具有一个典型的先验理性主义视角。我们从语言学和心理学现有文献的视角以及基于实验研究LLMs学习可能和不可能语言能力的角度审视这一著名批评。我们通过将英语应用特定转换生成了语法上不可能的语言，包括反转整个句子和根据词数奇偶性添加否定。在GPT-2小模型和长短期记忆（LSTM）模型上进行了两轮受控实验。单次运行训练轨迹的描述性分析显示，GPT-2小模型在自然语言任务上的最终损失较低、收敛速度更快、困惑度较低，其中反转条件表现最差（损失比自然语言高达2.25倍）。LSTM模型在不同条件下则差异最小。鉴于实验的单次运行性质（每种条件n=1），我们报告了描述性比较，并提醒正式统计推断无法进行。基于理论分析和描述性实证发现，我们提出了一种新的Chomsky理论视角，即在LLMs研究中从Chomsky的“理性主义-浪漫主义”范式转向功能主义和经验主义。
英文摘要：
In Chomsky's provocative critique "The False Promise of CHATGPT," Large Language Models (LLMs) are characterized as mere pattern predictors that do not acquire languages via intrinsic causal and self-correction structures like humans, therefore are not able to distinguish impossible languages. It stands as a representative in a fundamental challenge to the intellectual foundations of AI, for it integrally synthesizes major issues in methodologies within LLMs and possesses an iconic a priori rationalist perspective. We examine this famous critique from both the perspective in pre-existing literature of linguistics and psychology as well as a research based on an experiment inquiring into the capacity of learning both possible and impossible languages among LLMs. We constructed a set of syntactically impossible languages by applying certain transformations to English. These include reversing whole sentences, and adding negation based on word-count parity. Two rounds of controlled experiments were each conducted on GPT-2 small models and long short-term memory (LSTM) models. Descriptive analysis of single-run training trajectories shows that GPT-2 small models exhibit lower final loss, faster convergence, and lower perplexity on natural language compared to impossible language conditions, with the reversed condition showing the largest departure (loss ratios up to 2.25 * natural). LSTM models, by contrast, show minimal differences across conditions. Given the single-run nature of our experiments (n=1 per condition), we report descriptive comparisons and caution that formal statistical inference is precluded. Based on theoretical analysis and descriptive empirical findings, we propose a new vision within Chomsky's theory towards LLMs, and a shift of theoretical paradigm outside Chomsky, from his "rationalist-romantics" paradigm to functionalism and empiricism in LLMs research.
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