Emergent Abilities of Large Language Models

The phenomenon of "emergent abilities" in large language models refers to the observation that as these models increase in size, they begin to exhibit new and unexpected capabilities not present in smaller versions. One example is a model's ability to perform multi-step reasoning, which can improve its performance on tasks like arithmetic or complex instruction following. Several factors may contribute to emergent abilities in large language models. Scaling up model size has been shown to increase their performance on various benchmarks. Additionally, increasing the amount of training data can lead to improved performance and potentially reveal new abilities. However, building larger models also requires more computational resources and generates higher costs. There are limitations to scaling up models in search of emergent abilities. The most significant limitation is the availability of high-quality training data. Even if a model is large enough to exhibit emergent abilities, it may not be able to effectively utilize them due to insufficient or low-quality training data. Therefore, while larger language models have shown promise for revealing new capabilities, there are still practical considerations and limitations that must be addressed.