Mike Conover: recommendation

Transcript context

…How does that tie into one of our other favorite topics, e-mails? We had David Luan from Adapt and he mentioned they don't care about benchmarks because their customers don't work on benchmarks, they work on business results. How do you think about that for you? And maybe as you build a new company, when is the time to like still focus on the benchmark versus when it's time to like move on to your own evaluation using maybe labelers or whatnot? We use a fair bit of LLM supervision to evaluate multiple different subsystems. And I think that one of the reasons that we pay human annotators to evaluate the quality of the generative outputs, and I think that that is always the reference standard, but we frequently first turn to LLM supervision as a way to have, whether it's at fine-tuning time or even for subsystems that are not generative, what is the quality of the system? I think we will generate a small corpus of high-quality domain expert annotations and always compare that against how well is either LLM supervision or even just a heuristic. A simple thing you can do, this is a technique that we do not use, but as an example, do not generate any integers or any numbers that are not present in the underlying source data. If they're doing rag, you can just say you can't name numbers that are not, it's very sort of heavy-handed, but you can take the annotations of a human evaluator and then compare that. I mean, Snorkel kind of takes a similar perspective, like multiple different weak sort of supervision data sets can give you substantially more than any one of them does on their own. And so I think you want to compare the quality of any evaluation against human-generated sort of benchmark. But at the end of the day, especially for things that are nuanced, is this transcendent poetry, there's just no way to multiple choice your way out of that, you know? And so really where I think a lot of the flywheels for some of the large LLM companies are, it's methodological, obviously, but it's also just data generation. And you think about like, you know, for anybody who's done crowdsource work, and this I think applies to the high-skilled human annotators as well, like you look at the Google search quality evaluator guidelines, it's like a 90 or 120-page rubric describing like, what is a high-quality Google search result? And it's like very difficult to get on a human level people to reproducibly follow a rubric. And so what is your process for orchestrating that motion? Like how do you articulate what is high-quality insight? I think that's where a lot of the work actually happens, and that it's sort of the last resort. Ideally, you want to automate everything, but ultimately the most interesting problems right now are those that are not especially automatable. One thing you did at Databricks was the, well, not that you did specifically, but the team there was like the Dolly 15K dataset. You mentioned people misvalue the value of this data. Why has no other company done anything similar with like creating this employee-led dataset? You can imagine some of these Goldman Sachs, they got like thousands and thousands of people in there. Obviously they have different privacy and whatnot requirements. Do you think more companies should do it? Do you think there's like a misunderstanding of how valuable that is?…

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