Enhancing AI with SynPO
Aligning AI with Human Preferences
Recent advancements in Large Language Models (LLMs) have focused on producing honest, safe, and useful responses. This alignment helps models understand what humans find important in their interactions. However, maintaining this alignment is challenging due to the high costs and time required to gather quality data.
Introducing SynPO
What is SynPO?
SynPO, or Synthetic Preference Optimisation, is a unique method designed to improve LLM alignment without relying heavily on human input. It creates synthetic data through a self-boosting process, allowing models to learn and improve iteratively.
Key Components of SynPO
1. Self-Prompt Generator:
This component generates various prompts using the model’s own capabilities. It creates diverse scenarios for the model to explore, enriching the training environment without needing complex datasets.
2. Response Improver:
The response improver enhances the model’s outputs by refining its responses. It identifies weaknesses in initial replies and guides the model to produce better answers, teaching it what constitutes a quality response.
Benefits of SynPO
By combining these components, SynPO allows LLMs to learn from synthetic feedback loops. This self-driven approach significantly reduces the need for manual data labeling, making it more efficient and scalable.
SynPO has shown impressive results, improving LLMs like Llama3-8B and Mistral-7B after just a few iterations. These models have increased their success rates by over 22.1% on evaluation benchmarks and improved their scores on the Open LLM leaderboard.
Summary of Contributions
- SynPO generates high-quality synthetic training data, enhancing the variety and quality of prompts and responses.
- It enables LLMs to learn from feedback, progressively improving their outputs.
- LLMs show significant performance gains after three to four iterations, demonstrating the effectiveness of this method.
Conclusion
SynPO offers a cost-effective way to enhance LLMs without the traditional expenses of data collection. Through iterative self-training and synthetic data, LLMs can continuously evolve, aligning more closely with human preferences and adapting to various applications.
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