Shawn Wang: evaluation

Transcript context

…Oh, yeah, yeah, exactly. Right, right, right. Yeah. Yeah. When we launched this workflow, now that GPT-4 was available, basically Elisa was at a place where we have very tabular interfaces. So given a table of papers, you can extract data across all the tables. But you kind of want to take the analysis a step further. Sometimes what you'd care about is not having a list of papers, but a list of arguments, a list of effects, a list of interventions, a list of techniques. And so that's one of the things we're working on is now that you've extracted this information in a more structured way, can you pivot it or group by whatever the information that you extracted to have more insight first information still supported by the academic literature? Yeah, that was a big revelation when I saw it. Basically, I think I'm very just impressed by how first principles, your ideas around what the workflow is. And I think that's why you're not as reliant on like the LLM improving, because it's actually just about improving the workflow that you would recommend to people. Today we might call it an agent, I don't know, but you're not relying on the LLM to drive it. It's relying on this is the way that Elicit does research. And this is what we think is most effective based on talking to our users. The problem space is still huge. Like if it's like this big, we are all still operating at this tiny part, bit of it. So I think about this a lot in the context of moats, people are like, oh, what's your moat? What happens if GPT-5 comes out? It's like, if GPT-5 comes out, there's still like all of this other space that we can go into. So I think being really obsessed with the problem, which is very, very big, has helped us like stay robust and just kind of directly incorporate model improvements and they keep going.…

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