#PHENOMENA MOVIE LANGUAGES FULL#

We call a prompted task emergent when it unpredictably surges from random performance to above-random at a specific scale threshold. The model is given one example of a task (classifying a movie review as positive or negative) and then performs the task on an unseen example. Without any further fine-tuning, language models can often perform tasks that were not seen during training.Įxample of few-shot prompting on movie review sentiment classification. In such tasks, a pre-trained language model is given a prompt for a task framed as next word prediction, and it performs the task by completing the response. The existence of such emergent abilities raises the question of whether additional scaling could potentially further expand the range of capabilities of language models.įirst we discuss emergent abilities that may arise in prompted tasks.

Overall, we present dozens of examples of emergent abilities that result from scaling up language models.

#PHENOMENA MOVIE LANGUAGES FULL#

However, we also explore emergence as a function of other variables, such as dataset size or number of model parameters (see the paper for full details). More specifically, we study emergence by analyzing the performance of language models as a function of language model scale, as measured by total floating point operations (FLOPs), or how much compute was used to train the language model. In “ Emergent Abilities of Large Language Models,” recently published in the Transactions on Machine Learning Research (TMLR), we discuss the phenomena of emergent abilities, which we define as abilities that are not present in small models but are present in larger models. Given the growing use of language models in NLP research and applications, it is important to better understand abilities such as these that can arise unexpectedly. For example, the GPT-3 paper showed that the ability of language models to perform multi-digit addition has a flat scaling curve (approximately random performance) for models from 100M to 13B parameters, at which point the performance jumped substantially. On the other hand, performance for certain other tasks does not improve in a predictable fashion. For instance, the effect of scale on language model perplexity has been empirically shown to span more than seven orders of magnitude. In many cases, the performance of a large language model can be predicted by extrapolating the performance trend of smaller models. Scaling up the size of language models often leads to improved performance and sample efficiency on a range of downstream NLP tasks. The field of natural language processing (NLP) has been revolutionized by language models trained on large amounts of text data. Posted by Jason Wei and Yi Tay, Research Scientists, Google Research, Brain Team

Phenomena movie languages