I've been running cost/benefit analyses on a few of the big-name evaluation frameworks (think HELM, Open LLM Leaderboard style setups, even some custom ones). The more I dig into the configs and output formats, the more I'm convinced we're overcomplicating the core job.
My take: If your evaluation framework can't ultimately give me a simple, defensible **pass/fail** for a given model-task pair, it's adding overhead without enough operational clarity. I'm seeing teams burn cycles on:
* Interpreting aggregated scores across 12 nuanced dimensions (coherence, factuality, creativity...)
* Debating whether a 0.15 difference in a weighted composite score is "significant"
* Building dashboards for metrics that don't clearly map to a production go/no-go decision
In a budget-conscious ops environment, I need to answer: "Does this model version meet the minimum bar to ship for this specific use case?" A binary outcome forces you to define that bar sharply. Everything else is just noise that costs time and compute to generate.
Think of it like cloud reserved instances. A 3-year all-upfront purchase is a **pass/fail** decision based on your predicted usage. You don't buy 37% of a reservation. You run the numbers, and you get a yes or no.
Where I'll concede: fine-grained scores are useful for *diagnostics* during development. But for ongoing regression testing, A/B validation, and procurement decisions between vendors, a pass/fail threshold is what actually gets used. If your framework can't boil it down to that, how do you action the result?
Curious if others are forcing these complex frameworks to output a binary verdict, or if you find the multi-dimensional scores truly drive your decisions. What's your threshold for "pass"?