Benjamin Searle

Building such domain experts would likely require developing an artificial general intelligence, which is beyond the scope of this paper. Instead, by “domain expert” we specifically mean that the system is capable of producing results (with or without actual “understanding”) that are of the same quality as a human expert in the given domain.

[Curator's note: there are numerous other technical challenges  addressed with alternative prescriptions throughout the paper. These highlights are narrative-centric, and I invite you to review the paper if you are a keen technologist looking for answers to the following:- Zero- and Few-Shot Learning - Response Generation- Arith... See more

- The problem is one of content. The misconception is that without deep content, design is reduced to pure style, a bag of dubious tricks. In graphic-design circles, form-follows-function is reconfigured as form-follows-content. If content is the source of form, always preceding it and imbuing it with meaning, form without content (as if that were ... See more

This represents a fundamentally different way of thinking about IR systems. Within the index-retrieve-then-rank paradigm, modeling work (e.g., query understanding, document understanding, retrieval, ranking, etc.) is done on top of the index itself. This results in modern IR systems being comprised of a disparate mix of heterogeneous models (e.g., ... See more

When experiencing an information need, users want to engage with a domain expert, but often turn to an information retrieval (IR) 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.