crowdworker-annotated data, however, turns out to be an effective method for improving all metrics. In some cases,
ﬁne-tuning these same models allows us to obtain results equivalent to having a signiﬁcantly larger model. For example,
in the case of sensibleness, we may need a dense model that is multiple orders of magnitude larger than the 137B
parameters PT model in order to reach the 92.3% sensibleness achieved by LaMDA, which is a ﬁne-tuned version of PT.
Note that in several metrics, our ﬁne-tuned models almost reach the crowdworker quality levels, and our ﬁne-tuned
models exceed crowdworker quality for interestingness (labeled ‘Human’ in Figures 4 and 5). However, this may be a
weak baseline as crowdworkers are not extensively trained and were not incentivized to generate high-quality responses.
For example, it turns out it is quite difﬁcult to generate very interesting responses given limited ﬁnancial incentives,
LaMDA- Language Models for Dialog Applications