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The UK gov is probably talking about using embeddings to respond to FOI requests. 1000000 documents -> 1000000000 embeddings Citizen question -> GPT normalise

by sgt101 3y ago

The UK gov is probably talking about using embeddings to respond to FOI requests. 1000000 documents -> 1000000000 embeddings Citizen question -> GPT normalised question -> 'embeddings match 'embeddings <-> embeddings recover document fragments with matched embeddings use GPT to create answer from document fragments The question is how successfully this process creates the answers required. Who knows? But, I would not be surprised if it worked pretty well and it might boost productivity to the point where there's a massive saving to be had. Maybe - but that's the fun!

IanCal 3y ago
I doubt it. The article mentions generating answers based on sources like Hansard, and dealing with large numbers of consultation responses. Frankly it's shocking they haven't done any ai stuff with consultation responses, lots of freeform text is where you usually want to start doing clustering and analysis.
- sgt101 3y ago
  Are you saying that you don't think that embedding retrieval would work well for this kind of problem or are you saying that you doubt that the UK gov will be doing this kind of thing. BTW - I used to do lots of clustering and analysis on freeform text, but first SVD and then embeddings have just changed the game so much that it's the starting point now.
  - IanCal 3y ago
    That the UK gov will be doing that, certainly from the descriptions in the article - though perhaps I've misunderstood what you meant. If it's "backend tool to more easily find relevant sources" rather than what I originally thought which was "automated FOI responses with minimal/no human oversight" then I could see that more. The examples in the article were a backend tool for searching hansard and one for what sounds like clustering and summarising consultation responses. I think the latter was misunderstood by the author of the submission to mean responding to citizens rather than processing citizen responses. > BTW - I used to do lots of clustering and analysis on freeform text, but first SVD and then embeddings have just changed the game so much that it's the starting point now. I agree, also with things like naming clusters and summarising what a cluster is about is a huge improvement IMO to results.

by sgt101 3y ago

IanCal 3y ago

I doubt it. The article mentions generating answers based on sources like Hansard, and dealing with large numbers of consultation responses. Frankly it's shocking they haven't done any ai stuff with consultation responses, lots of freeform text is where you usually want to start doing clustering and analysis.

sgt101 3y ago
Are you saying that you don't think that embedding retrieval would work well for this kind of problem or are you saying that you doubt that the UK gov will be doing this kind of thing. BTW - I used to do lots of clustering and analysis on freeform text, but first SVD and then embeddings have just changed the game so much that it's the starting point now.
- IanCal 3y ago
  That the UK gov will be doing that, certainly from the descriptions in the article - though perhaps I've misunderstood what you meant. If it's "backend tool to more easily find relevant sources" rather than what I originally thought which was "automated FOI responses with minimal/no human oversight" then I could see that more. The examples in the article were a backend tool for searching hansard and one for what sounds like clustering and summarising consultation responses. I think the latter was misunderstood by the author of the submission to mean responding to citizens rather than processing citizen responses. > BTW - I used to do lots of clustering and analysis on freeform text, but first SVD and then embeddings have just changed the game so much that it's the starting point now. I agree, also with things like naming clusters and summarising what a cluster is about is a huge improvement IMO to results.