Dialog collection: We collect 40K annotated dialog turns annotated (generative data). We also collect 9K dialog
turns, in which the LaMDA’s generated candidates are labeled ‘correct’ or ‘incorrect’, to be used as input data for the
ranking task (discriminative data).
We collect a set of human-human dialogs between crowdworkers, focused on information-seeking interactions, and
evaluate whether their statements can be supported by known authoritative sources. As seen in Figure 4, it is notable
that they make well-supported claims at a higher rate if they have access to TS. When asked for Rafael Nadal’s age, a
human expert may not know the answer immediately, but can easily query an information retrieval system to obtain it.
Therefore, we decided to ﬁne-tune our language model to provide attributions for its responses by looking up its claims
using a toolset.
To collect training data for the ﬁne-tuning used in the algorithm, we use both static and interactive methods again.
LaMDA- Language Models for Dialog Applications