Techniques for ensuring large language models remain aligned and safe for enterprise deployment.
As LLMs are integrated into critical business processes, ensuring their reliability and safety is paramount. Our research focuses on practical techniques for "red teaming" and alignment.
We are experimenting with "Constitutional AI" approaches, where models are trained to critique and revise their own outputs based on a set of high-level principles. This self-correction mechanism has shown promise in reducing harmful outputs without heavy-handed filtering.
Automated adversarial testing is crucial. We are developing agents specifically designed to probe LLMs for vulnerabilities, trying to elicit PII, hate speech, or hallucinated facts. This continuous "red teaming" helps us identify weaknesses before deployment.