Jungwon Byun: evaluation

Transcript context

…I think the most powerful feature of Elicit is the ability to extract, add columns to this table, which effectively extracts data from all of your papers at once. It's well used, but there are kind of many different extensions of that that I think users are still discovering. So one is we let you give a description of the column. We let you give instructions of a column. We let you create custom columns. So we have like 30 plus predefined fields that users can extract, like what were the methods? What were the main findings? How many people were studied? And we actually show you basically the prompts that we're using to extract that from our predefined fields. And then you can fork this and you can say, oh, actually I don't care about the population of people. I only care about the population of rats. Like you can change the instruction. So I think users are still kind of discovering that there's both this predefined, easy to use default, but that they can extend it to be much more specific to them. And then they can also ask custom questions. One use case of that is you can start to create different column types that you might not expect. So instead of just creating generative answers, like a description of the methodology, you can say classify the methodology into a prospective study, a retrospective study, or a case study. And then you can filter based on that. It's like all using the same kind of technology and the interface, but it unlocks different workflows. So I think that the ability to ask custom questions, give instructions, and specifically use that to create different types of columns, like classification columns, is still pretty underrated. In terms of use case, I spoke to someone who works in medical affairs at a genomic sequencing company recently. So doctors kind of order these genomic tests, these sequencing tests, to kind of identify if a patient has a particular disease. This company helps them process it. And this person basically interacts with all the doctors and if the doctors have any questions. My understanding is that medical affairs is kind of like customer support or customer success in pharma. So this person like talks to doctors all day long. One of the things they started using Elicit for is like putting the results of their tests as the query. Like this test showed, you know, this percentage presence of this and 40% that and whatever, you know, what genes are present here or what's in this sample. And getting kind of a list of academic papers that would support their findings and using this to help doctors interpret their tests. So we talked about, okay, cool, like if we built, he's pretty interested in kind of doing a survey of infectious disease specialists and getting them to evaluate, you know, having them write up their answers, comparing it to Elicit's answers, trying to see can Elicit start being used to interpret the results of these diagnostic tests. getting them to evaluate, you know, having them write up their answers, comparing it to Elicit's answers, trying to see can Elicit start being used to interpret the results of these diagnostic tests. Because the way they ship these tests to doctors is they report on a really wide array of things. He was saying that at a large, well-resourced hospital, like a city hospital, there might be a team of infectious disease specialists who can help interpret these results. But at under-resourced hospitals or more rural hospitals, the primary care physician can't interpret the test results, so then they can't order it, they can't use it, they can't help their patients with it. So thinking about an evidence-backed way of interpreting these tests is definitely kind of an extension of the product that I hadn't considered before. But yeah, the idea of using that to bring more access to physicians in all different parts of the country and helping them interpret complicated science is pretty cool. Yeah. We had Kanjun from Imbue on the podcast and we talked about better allocating scientific resources. How do you think about these use cases and maybe how illicit can help drive more research? And do you see a world in which maybe the models actually do some of the research before suggesting us?…

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