OpenAI’s system works in two stages; first we train a transformer model on a very large amount of data in an unsupervised manner — using language modeling as a training signal — then we fine-tune this model on much smaller supervised datasets to help it solve specific tasks. We developed this approach following our sentiment neuron work, in which we noted that unsupervised learning techniques can yield surprisingly discriminative features when trained on enough data. Here, we wanted to further explore this idea: can we develop one model, train it in an unsupervised way on a large amount of data, and then fine-tune the model to achieve good performance on many different tasks? Our results indicate that this approach works surprisingly well; the same core model can be fine-tuned for very different tasks with minimal adaptation.
This work builds on the approach introduced in Semi-supervised Sequence Learning, which showed how to improve document classification performance by using unsupervised pre-training of an LSTM followed by supervised fine-tuning. It also extends ULMFiT, research that shows how a single dataset-agnostic LSTM language model can be fine-tuned to get state-of-the-art performance on a variety of document classification datasets; our work shows how a Transformer-based model can be used in this approach to succeed at a broader range of tasks beyond document classification, such as commonsense reasoning, semantic similarity, and reading comprehension. It is also similar to but more task-agnostic than ELMo, which incorporates pre-training but uses task-customized architectures to get state-of-the-art results on a broad suite of tasks.
Very little tuning was used to achieve our results. All datasets use a single forward language model, without any ensembling, and the majority of the reported results use the exact same hyperparameter settings.
A result we are particularly excited about is the performance of our approach on three datasets — COPA, RACE, and ROCStories — designed to test commonsense reasoning and reading comprehension. Our model obtains new state-of-the-art results on these datasets by a wide margin. These datasets are thought to require multi-sentence reasoning and significant world knowledge to solve suggesting that our model improves these skills predominantly via unsupervised learning. This suggests there’s hope for developing complex language understanding capabilities via unsupervised techniques.
Why Unsupervised Learning?
Supervised learning is at the core of most of the recent success of machine learning. However, it can require large, carefully cleaned, and expensive to create datasets to work well. Unsupervised learning is attractive because of its potential to address these drawbacks. Since unsupervised learning removes the bottleneck of explicit human labeling it also scales well with current trends of increasing compute and availability of raw data. Unsupervised learning is a very active area of research but practical uses of it are often still limited.
There’s been a recent push to try to further language capabilities by using unsupervised learning to augment systems with large amounts of unlabeled data; representations of words trained via unsupervised techniques can use large datasets consisting of terabytes of information and, when integrated with supervised learning, improve performance on a wide range of NLP tasks. Until recently, these unsupervised techniques for NLP (for example, GLoVe and word2vec) used simple models (word vectors) and training signals (the local co-occurence of words). Skip-Thought Vectors is a notable early demonstration of the potential improvements more complex approaches can realize. But new techniques are now being used which are further boosting performance. These include the use of pre-trained sentence representation models, contextualized word vectors (notably ELMo and CoVE), and approaches which use customized architectures to fuse unsupervised pre-training with supervised fine-tuning, like our own.