Given the harm-inducing queries, we probe our Gemini models and analyze the model responses
via side-by-side evaluation. As discussed above, we balance the objective of model output response
being harmless versus being helpful. From the detected risk areas, we create additional supervised
fine-tuning data to demonstrate the desirable responses. To generate such responses at scale, we
heavily rely on a custom data generation recipe loosely inspired from Constitutional AI (Bai et al.,
2022b), where we inject variants of Google’s content policy language as “constitutions”, and utilize
language model’s strong zero-shot reasoning abilities (Kojima et al., 2022) to revise responses and
choose between multiple response candidates. We have found this recipe to be effective – for example
in Gemini Pro, this overall recipe was able to mitigate a majority of our identified text harm cases,
without any perceptible decrease on response helpfulness.
gemini_1_report