sources, rather than trained to use an intermediate tool. Given these differences, we note that the range of existing
approaches to this problem include the RNNLM [34], RAG [35], REALM [36], and FiD [37] architectures. Zhu et
al. [38] provide a survey of further recent work. See Karpukhin et al. [39] for details on the ‘dense passage retriever’
used in RAG. Recent work in this direction has expanded and elaborated on neural models’ ability to retrieve and rank
passages [40]. The RETRO architecture demonstrates that language models can be primed with results retrieved from
a database as large as two trillion tokens [41]. At a broad level, our approach is also comparable to that of Byrne et
al. [42], which ﬁne-tunes the model to use external APIs for movie ticketing dialog.
Parts of our ﬁndings are similar to recent studies on dialog groundedness. Granting access to external knowledge
LaMDA- Language Models for Dialog Applications