In search, judgment lists fall apart. It’s frankly, humbling and humiliating :)

The work isn’t whatever tech returns search results. The work is the measurement - the evaluation. Everything else becomes incidental in search.

Better evals has been my humbling lesson about from industry. Humbling because its not exciting or sexy, but because its hard. And its the work nobody wants to do. Everyone wants to do .

For this reason, almost everyone overreads the value of some naive judgment list method. Almost nobody wants to understand limitations. The casual judgment list our team put together by hand, the crowdsourced one from experts, the clickstream based. None of which I’d trust beyond well-scoped problems. It’s easy to cherry pick when one approach worked, but ignore when it failed. It’s harder to admit our systems of evaluation have deep, fundamental flaws no miracle fix can cure.

That all has to change if you train a model on user behaviors. Then this becomes the REAL work of search. It’s not whether you choose LambdaMART or a cross encoder or a bi encoder or 512 trained hamsters. The real modeling work is “does this floating point accurately describe whether a real user thinks this result is relevant or not”

That’s really hard, underappreciated, and not sexy work. If that interests you, please come hang out at Cheat at Search Essentials tomorrow and learn about evals with me and chat evals :) I’ll try to share the theory, the practice, and the practical, dumb, grug based systems that actually work

• If this interests you, come to today's talk on search evaluation, 12:30PM ET - https://maven.com/p/2ecb67/cheat-at-search-essentials-evaluation-ndcg-and-pals

-Doug

PS: 6 days left for Cheat at Search with Agents - http://maven.com/softwaredoug/cheat-at-search​

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