• FT quality-safety: ﬁne-tune the pre-trained model (PT) to train discriminators that predict quality and safety
labels. The generated candidate responses are ﬁltered at inference time by their safety scores, and re-ranked by
a weighted sum of the three quality score types. PT is also ﬁne-tuned to generate in-context responses from
a clean sample of pre-training dialog data ﬁltered using LaMDA discriminators. See Section 6.1 for more
details.

• FT groundedness (LaMDA): ﬁne-tune FT quality-safety to generate calls to an external information retrieval
system to provide attributed responses. The model is also ﬁne-tuned to jointly predict the quality and the type
(i.e., calling a certain tool or replying to the user) of the next action. See Section 6.2 for more details.

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Table 1: Summary of the datasets to improve safety, groundedness, and quality.
LaMDA- Language Models for Dialog Applications