论文标题
部分可观测时空混沌系统的无模型预测
Can language models handle recursively nested grammatical structures? A case study on comparing models and humans
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
How should we compare the capabilities of language models (LMs) and humans? I draw inspiration from comparative psychology to highlight some challenges. In particular, I consider a case study: processing of recursively nested grammatical structures. Prior work suggests that LMs cannot handle these structures as reliably as humans can. However, the humans were provided with instructions and training, while the LMs were evaluated zero-shot. I therefore match the evaluation more closely. Providing large LMs with a simple prompt -- substantially less content than the human training -- allows the LMs to consistently outperform the human results, and even to extrapolate to more deeply nested conditions than were tested with humans. Further, reanalyzing the prior human data suggests that the humans may not perform above chance at the difficult structures initially. Thus, large LMs may indeed process recursively nested grammatical structures as reliably as humans. This case study highlights how discrepancies in the evaluation can confound comparisons of language models and humans. I therefore reflect on the broader challenge of comparing human and model capabilities, and highlight an important difference between evaluating cognitive models and foundation models.
