Large language models have exhibited emergent abilities, demonstrating exceptional performance across diverse tasks for which they were not explicitly trained, including those that require complex reasoning abilities. The emergence of such abilities carries profound implications for the future direction of research in NLP, especially as the deployment of such models becomes more prevalent. However, one key challenge is that the evaluation of these abilities is often confounded by competencies that arise in models through alternative prompting techniques, such as in-context learning and instruction following, which also emerge as the models are scaled up. In this study, we provide the first comprehensive examination of these emergent abilities while accounting for various potentially biasing factors that can influence the evaluation of models. We conduct rigorous tests on a set of 18 models, encompassing a parameter range from 60 million to 175 billion parameters, across a comprehensive set of 22 tasks. Through an extensive series of over 1,000 experiments, we provide compelling evidence that emergent abilities can primarily be ascribed to in-context learning. We find no evidence for the emergence of reasoning abilities, thus providing valuable insights into the underlying mechanisms driving the observed abilities and thus alleviating safety concerns regarding their use.

翻译：大语言模型展现出涌现能力，在未经过明确训练的各类任务中表现出色，包括需要复杂推理能力的任务。这些能力的涌现对自然语言处理领域的未来研究方向具有深远影响，尤其是在此类模型的部署日益普遍的情况下。然而，一个关键挑战是，这些能力的评估常常受到模型通过替代提示技术（如上下文学习和指令遵循）所获得的能力的混淆，而这些技术本身也会随着模型的规模扩大而涌现。在本研究中，我们首次全面考察了这些涌现能力，同时考虑了可能影响模型评估的各种潜在偏置因素。我们对涵盖6000万到1750亿参数范围的18个模型，在22项任务的全面集合上进行了严格测试。通过超过1000次实验的广泛系列，我们提供了有力证据，表明涌现能力主要可归因于上下文学习。我们没有发现推理能力涌现的证据，从而为观察到的能力背后的底层机制提供了宝贵见解，并因此减轻了对其使用的安全担忧。