Large language models (LLMs) are becoming increasingly powerful, but they can also be unsafe if not properly aligned with human values. OpenAI’s new research on deliberative alignment is a promising approach to making LLMs safer.
What is deliberative alignment?
Deliberative alignment is a training paradigm that teaches LLMs to reason about human-written safety specifications before responding to prompts. This is done by training the LLMs on a dataset of text and code that includes safety principles and best practices.
How does deliberative alignment work?
When an LLM is trained using deliberative alignment, it learns to do two things:
- Identify relevant safety principles: The LLM can identify the safety principles that are most relevant to a given prompt.
- Reason about how to apply those principles: The LLM can then reason about how to apply those principles to generate a safe and appropriate response.
Benefits of deliberative alignment
Deliberative alignment has several potential benefits, including:
- Improved safety: LLMs trained using deliberative alignment are less likely to generate harmful or unsafe outputs.
- Better transparency: Deliberative alignment can help to make LLMs more transparent by allowing humans to understand how they are reasoning about safety principles.
- Wider range of applications: By making LLMs safer, deliberative alignment could open up new applications for these powerful language models.
Challenges of deliberative alignment
There are also some challenges associated with deliberative alignment, such as:
- Developing effective safety specifications: It is important to develop safety specifications that are clear, concise, and comprehensive.
- Training LLMs to reason effectively: LLMs need to be trained to reason about safety principles in a way that is both accurate and efficient.
- Evaluating the safety of LLMs: It is important to develop methods for evaluating the safety of LLMs that have been trained using deliberative alignment.
