9 Discussion and limitations

Perhaps the most noteworthy aspect of our study is that signiﬁcant progress can be made towards better quality and
safer dialog models with modest amounts of human-annotated ﬁne-tuning data (less than 0.001% of pre-training data).
However, our study and LaMDA still have many limitations in spite of this progress.
Collecting ﬁne-tuning datasets brings the beneﬁts of learning from nuanced human judgements, but it is an expensive,
time consuming, and complex process. We expect results to continue improving with larger ﬁne-tuning datasets,
longer contexts, and more metrics that capture the breadth of what is required to have safe, grounded, and high quality
conversations. The complexity of capturing human subjective judgements limits the efforts that we took to assess
crowdworker rating quality against that of expert-annotated data, and to maximize clarity by iteratively designing

15
LaMDA- Language Models for Dialog Applications