OpenAI aims to prevent unwanted behavior in its AI models. They are using a method called Red-Teaming, where both humans and machines test the AI to identify potentially harmful or unwanted behaviors. This approach helps detect issues like producing harmful stereotypes, revealing private information, or generating fake content.
OpenAI has been transparent about its efforts. They published studies showing how they stress-test their language models. The goal is to find and fix behaviors that could be harmful or undesirable. Red-Teaming involves human testers who evaluate the models before they are released. Additionally, OpenAI is exploring ways to automate parts of this process using large language models like GPT-4.
The combination of human and machine testing aims to cover a wide range of behaviors. Human testers bring diverse perspectives, while automated testing can uncover many different behaviors. This method was first used by OpenAI in 2022 for the DALL-E 2 image generator.
OpenAI recruits a variety of experts to test their models, including artists, scientists, and legal professionals. These testers are encouraged to push the models to their limits to find new unwanted behaviors and ways to bypass existing safeguards. For instance, they might try to make ChatGPT say something racist or provoke DALL-E to create violent images.
Adding new features to a model can introduce new behaviors that need exploration. When OpenAI added a voice mode to GPT-4, testers found that the model sometimes imitated the speaker’s voice, which was unexpected and posed a security risk.
Automated Red-Teaming can cover a broader range of topics, but previous techniques had limitations. They often focused on either high-risk behaviors or discovered low-risk behaviors. The challenge is to find diverse and effective examples of potential issues.
OpenAI’s new approach separates the problem into two parts. They use a large language model to gather possible unwanted behaviors and then a reinforcement learning model to understand how these behaviors might occur. This method helps the model focus on a wider range of specific goals.
OpenAI tested this approach to detect indirect prompt injections, where external inputs try to manipulate the model. This was the first time automated Red-Teaming found such attacks.
Despite these efforts, no test can completely eliminate harmful behavior. There are countless ways users might misuse models, and new environments can change model behavior. External user groups should have tools to test large language models themselves.
Some researchers question the effectiveness of using GPT-4 for its own testing, as it might favor its outputs over others. Additionally, the rapid development and release of language models outpace the techniques for their evaluation.
OpenAI’s approach is a step forward, but more work is needed. The industry must rethink how they market these models, focusing on specific tasks rather than universal applications. Testing specific applications is crucial to understanding how well a model performs in real-world scenarios.
The idea is similar to saying a car engine is safe, so every car using it is safe, which is not necessarily true. Testing should focus on specific uses to ensure safety and effectiveness in actual environments.