Giving a domain a hill to climb: benchmarking as data activation

2 days ago

Benchmarking is a form of data activation, transforming domain data into measurable tasks for models to be evaluated and trained on.
Clear, verifiable metrics are essential for model improvement, as seen in coding and math, but complex domains like medicine lack inherent benchmarks.
Activation starts with measurement: converting health data into scores for models can reveal knowledge gaps and drive improvement, even without model changes.
Verifiers integrate benchmarking with reinforcement learning, turning scores into rewards and merging measurement with optimization, which amplifies both benefits and risks.
The key challenge is making domains amenable to scale by converting messy data into verifiable tasks, with benchmarking as a conversion method.
Different benchmark approaches exist on a spectrum: from expensive, raw-data-based methods (e.g., latchbio) to scalable, rubric-based ones (e.g., HealthBench) and lightweight multiple-choice setups (e.g., MedMarks).
QuestBench highlights the importance of noticing missing information, which mirrors real-world tasks like medical diagnosis where identifying gaps is crucial.
Curation remains a core challenge; expert judgment shapes benchmarks, but making it explicit in reward functions increases inspectability and transparency.
Benchmark flaws become incentives when used as rewards, potentially leading models to learn distorted behaviors, emphasizing the need for careful design.
Building benchmarks is a concrete form of data activation, involving creating verifiable tasks that enable scale to drive progress in complex domains.

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Giving a domain a hill to climb: benchmarking as data activation