Byun: Scientific meta-analysis typically takes five people over a year
Jungwon Byun · Supervise the Process of AI Research — with Jungwon Byun and Andreas Stuhlmüller of Elicit · Apr 11, 2024 · at 17:18
Jungwon Byun is the co-founder of Elicit, discussing the manual baseline workflow of scientific literature review that Elicit aims to automate.
0:00 / 0:37exact quote · 37.8s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“Lisset was very much inspired by this workflow in literature called systematic reviews or meta-analysis, which is basically the human state of the art for summarizing scientific literature. It typically involves like five people working together for over a year, and they kind of first start by trying to find the maximally comprehensive set of papers possible, so it's like 10,000 papers, and they kind of systematically narrow that down to like hundreds or 50 extract Key details from every single paper. Usually have two people doing it, like a third person reviewing it. So it's like an incredibly laborious, time consuming process, but you see it in every single domain.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Jungwon Byun
Byun: Supervising step-by-step AI reasoning makes models far easier to evaluate
“The importance of supervising the process of AI systems, not just the outcomes. And so a big part of how, then, like, how Elicit is built is, We're very intentional about not just throwing a ton of data into a model and training it and then saying, cool, here'…”
Byun: GPT-3 was a qualitative shift, while GPT-4 was an extension
“I think GPT-III was a big change because it kind of said, oh, now is the time to build to you that we can use AI to build these tools. And then GPT-IV was maybe a little bit more of an extension of GPT-III. It felt less like a level, GPT-III over GPT-II was li…”
Byun: Foundational models will not commoditize Elicit due to deep workflow specialization
“I think about this a lot in the context of moats. People are like, oh, what's your moat? What happens if GPT-V comes out? It's like, if GPT-V comes out, there's still like all of this other space that we can go into. And so I think being really obsessed with t…”
Byun: LLM self-reported uncertainty is reasonably well-calibrated in production
“We found it to be pretty calibrated. There varies on the model.”