Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations
This addresses the need for domain-specific alignment in AI applications, allowing tailored compliance with regulations, though it is incremental as it builds on existing alignment methods.
The paper tackles the problem of aligning large language models to specific contextual regulations, such as company policies, by introducing an Alignment Studio architecture with Framers, Instructors, and Auditors, enabling application developers to customize model behavior for particular values and norms.
The alignment of large language models is usually done by model providers to add or control behaviors that are common or universally understood across use cases and contexts. In contrast, in this article, we present an approach and architecture that empowers application developers to tune a model to their particular values, social norms, laws and other regulations, and orchestrate between potentially conflicting requirements in context. We lay out three main components of such an Alignment Studio architecture: Framers, Instructors, and Auditors that work in concert to control the behavior of a language model. We illustrate this approach with a running example of aligning a company's internal-facing enterprise chatbot to its business conduct guidelines.