alignment, at a high level.
The third metric, groundedness, is introduced for the model to produce responses that are grounded in known sources
wherever they contain veriﬁable external world information. Due to neural language models such as LaMDA’s capacity
to generalize rather than just memorize, they tend to generate responses that may seem plausible, but actually contradict
factual statements made in established sources. We use this metric for the model to avoid this tendency. While grounding
in known sources does not guarantee factual accuracy, it allows users or external systems to judge the validity of a
response based on the reliability of its source and its faithful reproduction. We ﬁnd that augmenting model outputs with
the ability to use external tools, such as an information retrieval system, is a promising approach to achieve this goal.
Therefore, we collect data from a setting where crowdworkers can use external tools to research factual claims, and
LaMDA- Language Models for Dialog Applications