The recent proliferation of research into transformer based natural language processing has led to a number of studies which attempt to detect the presence of human-like cognitive behavior in the models. We contend that, as is true of human psychology, the investigation of cognitive behavior in language models must be conducted in an appropriate population of an appropriate size for the results to be meaningful. We leverage work in uncertainty estimation in a novel approach to efficiently construct experimental populations. The resultant tool, PopulationLM, has been made open source. We provide theoretical grounding in the uncertainty estimation literature and motivation from current cognitive work regarding language models. We discuss the methodological lessons from other scientific communities and attempt to demonstrate their application to two artificial population studies. Through population based experimentation we find that language models exhibit behavior consistent with typicality effects among categories highly represented in training. However, we find that language models don't tend to exhibit structural priming effects. Generally, our results show that single models tend to over estimate the presence of cognitive behaviors in neural models.

翻译：近期基于Transformer的自然语言处理研究激增，催生了许多试图检测模型中是否存在类人认知行为的研究。我们认为，与人类心理学研究同理，对语言模型中认知行为的探究必须在适当规模的适恰群体中进行，才能使结果具有意义。我们利用不确定性估计领域的研究成果，提出了一种高效构建实验群体的新方法。由此开发的工具PopulationLM已开源。我们提供了基于不确定性估计文献的理论依据，并援引当前关于语言模型的认知研究作为动因。我们讨论了其他科学社区的方法论教训，并尝试将其应用于两项人工群体研究。通过基于群体的实验，我们发现语言模型表现出与训练中高频类别典型性效应相一致的行为。然而，语言模型并未展现出结构性启动效应。总体而言，我们的结果表明，单一模型往往会高估神经模型中的认知行为存在性。