our rating instructions. Furthermore, we did not examine patterns of disagreement between crowdworkers. Future
work will include selecting crowdworkers that mirror the system’s target users, and looking at ways to improve the
quality of labels, through training and evaluation approaches that also account for systematic disagreements between
crowdworkers due to social and cultural norms and values [99].
Fine-tuning can improve output groundedness, but the model can still generate responses that do not accurately reﬂect
the contents of authoritative external sources. Our progress on this has been limited to simple questions of fact, and
more complex reasoning remains open for further study (see example dialogs 15)). Similarly, while the model generates
responses that make sense most of the time, it can still suffer from subtler quality issues. For example, it may repeatedly
LaMDA- Language Models for Dialog Applications