This seems to be a promising approach!
"... How it works: The chatbot comprised two BART models. To train and test the system, the authors built a dataset of roughly 10,000 search-assisted dialogs. One human conversant chose a topic and started the conversation, while another, if necessary, queried a search engine and formulated replies. The authors tracked which statements led to a search, and which statements and searches led to which responses.
The authors trained one BART to take a dialogue-in-progress as input and generate the associated search query. The search engine returned five documents per query.
The authors trained the other BART to generate representations of each document and the dialog in progress, concatenate the representations, and generate the response. ..." (Source)
From the abstract:
"... we propose an approach that learns to generate an internet search query based on the context, and then conditions on the search results to finally generate a response, a method that can employ up-to-the-minute relevant information. We train and evaluate such models on a newly collected dataset of human-human conversations whereby one of the speakers is given access to internet search during knowledge driven discussions in order to ground their responses. We find that search-query based access of the internet in conversation provides superior performance compared to existing approaches that either use no augmentation or FAISS-based retrieval ..."
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