Key
1. Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks, but this paper discusses the unpredictable phenomenon of emergent abilities of large language models, which are not present in smaller models but are present in larger models.
2. Emergent abilities of large language models cannot be predicted simply by extrapolating the performance improvements on smaller-scale models.
3. The paper categorizes emergent abilities observed in a range of prior work, such as few-shot prompting and augmented prompting strategies.
4. Training computation, number of model parameters, and training dataset size are the primary factors in scaling today's language models.
5. Emergent abilities show a clear pattern where performance is near-random until a certain critical threshold of scale is reached, after which performance increases substantially.
6. The paper discusses specific emergent abilities observed in the few-shot prompting setting, including tasks from BIG-Bench and TruthfulQA benchmark.
7. It highlights the unpredictability of emergent abilities, which are not explicitly included in pre-training and are likely not fully explored in current language models.
8. The paper emphasizes the need for further research to understand why emergent abilities occur and addresses the potential implications and risks associated with emergent abilities in large language models.
9. The discussion of emergent abilities in language models raises important questions for the NLP field, which warrant careful study and investigation into future model capabilities and insights into training more-capable language models.
The research paper explores the impact of scale on language models in natural language processing (NLP), focusing on the unpredictable phenomenon of emergent abilities in large language models. It discusses how increasing the scale of language models can lead to better performance and sample efficiency on downstream NLP tasks. The paper delves into predicting the effect of scale on performance through scaling laws and the emergence of abilities in language models, drawing parallels with emergence in other domains such as physics, biology, and computer science. The study defines emergent abilities as those not present in smaller-scale models but present in large-scale models, highlighting the unpredictable nature of these emergent abilities.
Furthermore, it provides examples of emergent abilities observed in tasks such as few-shot prompting, augmented prompting strategies, multi-step reasoning, and instruction following. The paper also explores the emergence of socio-technical shifts towards general-purpose language models and the implications of scale on societal risks such as bias, toxicity, and truthfulness. Additionally, it discusses future research directions, including scaling models, improving model architectures and training, data scaling, better techniques for prompting, and understanding emergence.
The paper emphasizes the significance of studying emergent abilities in large language models and the need for further research to understand how and why they occur, as well as to predict the abilities of future models. The authors acknowledge the importance of studying emergent abilities and the associated risks, and highlight the need for careful consideration and further investigation in the field of NLP.
Reference: https://arxiv.org/abs/2206.07682