Key Points

1. Large language models trained on web data are computationally and data-intensive due to unstructured and noisy content, leading to challenges related to data scarcity and high training costs.

2. The proposed Web Rephrase Augmented Pre-training (WRAP) method utilizes an instruction-tuned model to rephrase web documents in different styles, such as "like Wikipedia" or in question-answer format, improving pre-training efficiency and model performance on out-of-distribution tasks.

3. The inclusion of synthetic data alongside real data allows training equivalent models with 5x lesser data or 3x lesser compute, outperforming models trained on real data alone.

4. Neural scaling laws suggest that synthetic data can enhance the learning process and is not merely another form of augmentation, and it can contribute to substantial performance gains in language model pre-training.

5. Synthetic data maintains similar semantic meaning as real data and primarily changes style, leading to improved model performance on specialized domains.

6. The cost of generating synthetic data is a one-time investment and is parallelizable, offering potential cost and scalability advantages over training solely on real data.

7. Synthetic data can be effectively leveraged to align language models to human values, eliminating the need for specific adjustments to the training algorithm.

8. Synthetic rephrases offer more value than mere repetition of existing data, especially in scenarios with scarce high-quality data, and for generalization on different text domains.

9. Synthetic data has the potential to improve language model training efficiency in terms of both compute and data size, offering promising opportunities for enhancing model performance and scalability.

Summary

Data Curation in Pre-Training Large Language Models
The research paper discusses the challenges and strategies for data curation in pre-training large language models (LLMs). It emphasizes the limitations of existing data curation techniques, the impact of model size on training compute and data size, and the use of synthetic data in pre-training LLMs through instruction fine-tuning and backtranslation. The paper also mentions the high cost and diminishing returns associated with re-training language models and the need for documenting effective data curation techniques despite the expenses.

Web Rephrase Augmented Pre-training (WRAP) Approach
The paper introduces the Web Rephrase Augmented Pre-training (WRAP) approach, which 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. The findings indicate that using WRAP on the C4 dataset speeds up pre-training by approximately 3 times and improves perplexity by more than 10% on average across different subsets of the Pile. Additionally, WRAP enhances zero-shot question-answer accuracy across 13 tasks by more than 2%.

Neural Scaling Laws and Dataset Selection
The paper also explores neural scaling laws for language models and discusses the importance of dataset selection, data augmentation, and synthetic data in enhancing LLM pre-training efficiency. Furthermore, it evaluates the impact of different rephrasing styles on the performance of LLMs and provides insights into the significance of real data and the combination of multiple styles in improving model performance.

The findings of the paper suggest that synthetic data, when generated with high quality and diversity, can significantly enhance LLM training efficiency and generalization across various tasks. However, the paper also highlights the challenges and limitations associated with the cost of generation and the need to enforce diversity in the generated data. Additionally, the paper notes the potential of synthetic data to improve LLM training efficiency and the importance of understanding the properties of the data fed to LLMs.

Reference: https://arxiv.org/abs/2401.16380