For open-domain QA, the models are not restricted to a specific domain. The texts are retrieved from sources such as books, the internet, Wikipedia, tables, graphs, knowledge bases, etc. (View Highlight)
In closed-domain QA, the models are geared towards a specific domain, such as documents focused on the legal and healthcare sectors. (View Highlight)
Open-book QA is where the model has access to external sources of data to answer questions, including Wikipedia, internal/company documents, etc. It is similar to open-book exams, where students can access information in their books (View Highlight)
In closed-book QA, the model responds to questions without being explicitly given a context because it has learned some information encoded into its parameters during training. (View Highlight)
This model doesn’t extract answers from a context; it generates text directly to answer the question using language models such as GPT-3 and optionally takes a context. (View Highlight)
The reader model then takes in the most similar contexts and the question to provide a span selection of the answer. (View Highlight)
We use this system to answer more abstract questions that don’t require exact answers. The system returns answers that are more suggestions or opinions, not direct. (View Highlight)
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