Fine-tuning: We then ﬁne-tune LaMDA to perform two tasks.
The ﬁrst task takes the multiturn dialog context to date and the response generated by the base model. It then generates
a special string (“TS” for toolset) indicating the following text is a query (e.g., “How old is Rafael Nadal?”) that should
be sent to the toolset: context + base → “TS, Rafael Nadal’s age”.
The second task takes the snippet returned by a tool, and a dialog statement (e.g., “He is 31 years old right now” +
“Rafael Nadal / Age / 35”). It then predicts the grounded version: context + base + query + snippet → “User, He is
35 years old right now”. Alternatively, this task can also output an additional research query. For example, context +
base + query + snippet → “TS, Rafael Nadal’s favorite song”. At inference time, the model’s output is directed to the
information retrieval system or to the user, depending on whether the ﬁrst generated string is ‘TS’ or ‘User’.
LaMDA- Language Models for Dialog Applications