Introduction
Large language models are the driving force behind many exciting applications in the world of artificial intelligence. From text generation to translation, these models are capable of processing and analyzing vast amounts of data with incredible precision. However, training these models requires enormous amounts of high-quality data, which is often difficult to come by. In this article, we’ll explore a new approach to large language model pre-training that’s more efficient and effective than traditional methods.
The Problem with Traditional Pre-training
Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such data requires an abundance of both compute and data, which grows with the size of the model being trained. This is infeasible both because of the large compute costs and duration associated with pre-training, and the impending scarcity of high-quality data on the web.
Solving the Problem with Web Rephrase Augmented Pre-training (WRAP)
In this work, we propose Web Rephrase Augmented Pre-training (WRAP) that uses an off-the-shelf instruction-tuned model prompted to paraphrase documents on the web in specific styles such as “like Wikipedia” or in “question-answer format” to jointly pre-train LLMs on real and synthetic rephrases.
Experimental Results
First, we show that using WRAP on the C4 dataset, which is naturally noisy, speeds up pre-training by ∼3x. At the same pre-training compute budget, it improves perplexity by more than 10% on average across different subsets of the Pile, and improves zero-shot question answer accuracy across 13 tasks by more than 2%. Second, we investigate the impact of the re-phrasing style on the performance of the model, offering insights into how the composition of the training data can impact the performance of LLMs in OOD settings.
Conclusion
In this article, we’ve explored a new approach to large language model pre-training that uses synthetic data to augment the training process. We’ve shown that this approach can significantly speed up pre-training, improve perplexity, and improve zero-shot question answer accuracy. By incorporating style diversity and higher quality synthetic data, we believe that WRAP can have a significant impact on the field of natural language processing.
Frequently Asked Questions
Q1: What is Web Rephrase Augmented Pre-training (WRAP)?
WRAP is a new approach to large language model pre-training that uses synthetic data to augment the training process. This approach prompts an off-the-shelf instruction-tuned model to paraphrase documents on the web in specific styles, such as “like Wikipedia” or in “question-answer format.”
Q2: What are the benefits of using WRAP?
The benefits of using WRAP include significant speedups in pre-training, improved perplexity, and improved zero-shot question answer accuracy. Additionally, WRAP provides style diversity and higher quality synthetic data, which can have a significant impact on the performance of LLMs in OOD settings.
Q3: How does WRAP differ from traditional pre-training methods?
WRAP differs from traditional pre-training methods in that it uses synthetic data to augment the training process. Traditional methods rely solely on real data, which can be noisy, unstructured, and poorly phrased. WRAP uses off-the-shelf instruction-tuned models to paraphrase documents in specific styles, resulting in higher quality and more diverse synthetic data.
Q4: Can WRAP be used with any language model?
WRAP can be used with any off-the-shelf instruction-tuned language model. However, the performance of WRAP may vary depending on the specific model and its capabilities. Our experiments were conducted using a large language model, but the results may be applicable to other models as well.
Q5: Are there any potential limitations of WRAP?
Potential limitations of WRAP include the need for a high-quality off-the-shelf instruction-tuned model, and the potential for overfitting on synthetic data. Additionally, the performance of WRAP may be limited by the quality of the original data used to create the synthetic data.
