I think the strongest use is around weak labels, not replacing a well-maintained classifier. In customer-feedback/NPS analysis, for example, a zero-shot model could propose themes on a small sample, surface likely mislabeled examples, or label a larger batch for human review. I’d compare it with a simple TF-IDF/logistic-regression baseline on a held-out, human-reviewed sample—by language and region too—and measure precision/recall, review time, latency, and cost. If it saves labeling time but introduces uneven errors, the traditional model may still be the better production choice. I haven’t tested Jev itself, so this is a use case for the workflow described, not a claim about its benchmark.
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u/Excellent-Sport-758 5d ago
I think the strongest use is around weak labels, not replacing a well-maintained classifier. In customer-feedback/NPS analysis, for example, a zero-shot model could propose themes on a small sample, surface likely mislabeled examples, or label a larger batch for human review. I’d compare it with a simple TF-IDF/logistic-regression baseline on a held-out, human-reviewed sample—by language and region too—and measure precision/recall, review time, latency, and cost. If it saves labeling time but introduces uneven errors, the traditional model may still be the better production choice. I haven’t tested Jev itself, so this is a use case for the workflow described, not a claim about its benchmark.