  When experiencing an information need, users want to engage with a domain
expert, but often turn to an information retrieval system, such as a search
engine, instead. Classical information retrieval systems do not answer
information needs directly, but instead provide references to (hopefully
authoritative) answers. Successful question answering systems offer a limited
corpus created on-demand by human experts, which is neither timely nor
scalable. Pre-trained language models, by contrast, are capable of directly
generating prose that may be responsive to an information need, but at present
they are dilettantes rather than domain experts -- they do not have a true
understanding of the world, they are prone to hallucinating, and crucially they
are incapable of justifying their utterances by referring to supporting
documents in the corpus they were trained over. This paper examines how ideas
from classical information retrieval and pre-trained language models can be
synthesized and evolved into systems that truly deliver on the promise of
domain expert advice.
