Instruction Pre-Training: Augmenting Language Models with Synthetic Data

This research paper dives into the world of supervised multitask learning for pre-training language models (LMs). It introduces a novel framework called Instruction Pre-Training, which utilizes synthetic data generated by an instruction synthesizer to enhance the training process.

• The paper highlights the limitations of unsupervised multitask pre-training, emphasizing the potential of supervised approaches, particularly in the post-training phase, for better generalization.
• Instruction Pre-Training aims to bridge this gap by augmenting raw corpora with instruction-response pairs, thereby providing a supervised learning signal for the LMs.

The Role of Synthetic Data

The core innovation lies in the instruction synthesizer, an efficient tool capable of generating diverse instruction-response pairs based on raw text. These pairs are then used to augment the corpora, effectively creating a vast collection of synthetic data for pre-training.

• The paper describes the process of converting various datasets into a format suitable for the instruction synthesizer, which is fine-tuned on this diverse collection to ensure its ability to generalize to unseen data.
• This data-driven approach allows for scalable task synthesis, covering a wide range of task categories, resulting in a significant boost to the pre-trained LMs' performance.

Instruction Pre-Training: A Step-by-Step Approach

The process of Instruction Pre-Training involves several key steps:

• Data Collection: A collection of context-based task completion datasets is carefully curated and formatted. Each example contains a piece of raw text and corresponding instruction-response pairs.
• Instruction Synthesizer Fine-Tuning: The instruction synthesizer, a language model itself, is fine-tuned on the curated dataset, learning to generate instruction-response pairs for any given raw text.
• Synthetic Data Generation: During inference, the fine-tuned instruction synthesizer generates a set of instruction-response pairs for each raw text in the pre-training corpora, effectively creating synthetic data.
• LM Pre-training: The LMs are trained on the augmented corpora, combining the original raw texts with the synthetic instruction-response pairs, thereby enriching the pre-training process with supervised signals.

Experimental Results: Unveiling the Benefits of Synthetic Data

Extensive experiments demonstrate the effectiveness of Instruction Pre-Training across various scenarios:

• General Pre-training From Scratch: The Instruction Pre-trained models significantly outperform vanilla pre-trained models, demonstrating data efficiency and improved performance on unseen tasks.
• Domain-Adaptive Continual Pre-training: Instruction Pre-Training consistently enhances the performance of existing LMs on domain-specific tasks, enabling smaller models to achieve parity with or even surpass larger models trained with vanilla methods.

Analysis: Understanding the Impact of Synthetic Data

The paper delves into analyzing the impact of synthetic data on LM pre-training:

• Instruction Synthesizer Evaluation: Experiments reveal the synthesizer's effectiveness in generating accurate and relevant instruction-response pairs for both seen and unseen datasets.
• Instruction-Augmented Corpora Analysis: Evaluation of the augmented corpora demonstrates the high quality of the synthetic data, ensuring context relevance, response accuracy, and diversity of tasks.

Conclusion: Embracing the Potential of Synthetic Data for Language Models

The paper concludes by emphasizing the significant potential of Instruction Pre-Training for enhancing the general abilities of LMs. The research highlights the benefits of leveraging synthetic data for supervised multitask pre-training, offering a promising path towards building more robust and capable language models.

• Instruction Pre-Training provides a powerful tool for bridging the gap between unsupervised and supervised multitask learning, effectively incorporating the benefits of both approaches.
• The paper encourages further exploration into this area, particularly focusing on addressing the limitations of synthetic data and optimizing the balance between quantity and quality.