Using one model for both generation and discrimination enables an efﬁcient combined generate-and-discriminate
procedure. After generating a response given a context, evaluating a discriminator involves computing P(“<desired-
rating>” | “<context> <sentinel> <response> <attribute-name>”). Since the model has already processed “<context>
<sentinel> <response>”, evaluating the discriminator simply involves processing a few additional tokens: “<attribute-
name> <desired rating>”.
First, we ﬁne-tune LaMDA to predict the SSI and safety ratings of the generated candidate responses. Then, we ﬁlter
out candidate responses for which the model’s safety prediction falls below a threshold during generation. Candidate
responses that remain after ﬁltering for safety are then ranked for quality. During ranking, sensibleness is given a
weight three times higher than speciﬁcity and interestingness, as this was found to work well for all metrics (i.e., 3 *
LaMDA- Language Models for Dialog Applications