Benchmark contamination and the case for domain-specific…
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Benchmark contamination and the case for domain-specific AI evaluation frameworks
There's growing evidence that popular LLM benchmarks (MMLU, HumanEval, SWE-Bench) suffer from contamination — models are increasingly trained on or tuned against benchmark data, inflating scores without corresponding real-world capability gains.
But there's a less discussed problem: even uncontaminated scores on these benchmarks don't transfer well to domain-specific operational tasks, particularly in regulated industries where correctness isn't optional.
I've been working on this problem in the lending/fintech space. A model that scores in the 90th percentile on general reasoning benchmarks can still fail basic mortgage underwriting tasks — misapplying regulatory thresholds, hallucinating compliance requirements, or misclassifying income documentation types.
This led me to try to build a benchmark, which evaluates LLM agents across a mortgage lifecycle. Some of the design challenges are interesting :
\- How do you construct evaluation tasks that are resistant to contamination when the domain knowledge is publicly available?
\- How do you benchmark multi-step agent workflows where errors compound (e.g. a misclassified document propagates through income verification → serviceability assessment → compliance check)?
\- How do you measure regulatory reasoning separately from general reasoning ability?
Early findings suggest model rankings shift considerably when moving from general to domain-specific evals, and that prompt architecture has an outsized effect relative to model selection.
For those interested repo is here: https://github.com/shubchat/loab
Happy to share more details if there's interest. Curious if anyone is working on similar evaluation methodology problems in other domains.
#technology
There's growing evidence that popular LLM benchmarks (MMLU, HumanEval, SWE-Bench) suffer from contamination — models are increasingly trained on or tuned against benchmark data, inflating scores without corresponding real-world capability gains.
But there's a less discussed problem: even uncontaminated scores on these benchmarks don't transfer well to domain-specific operational tasks, particularly in regulated industries where correctness isn't optional.
I've been working on this problem in the lending/fintech space. A model that scores in the 90th percentile on general reasoning benchmarks can still fail basic mortgage underwriting tasks — misapplying regulatory thresholds, hallucinating compliance requirements, or misclassifying income documentation types.
This led me to try to build a benchmark, which evaluates LLM agents across a mortgage lifecycle. Some of the design challenges are interesting :
\- How do you construct evaluation tasks that are resistant to contamination when the domain knowledge is publicly available?
\- How do you benchmark multi-step agent workflows where errors compound (e.g. a misclassified document propagates through income verification → serviceability assessment → compliance check)?
\- How do you measure regulatory reasoning separately from general reasoning ability?
Early findings suggest model rankings shift considerably when moving from general to domain-specific evals, and that prompt architecture has an outsized effect relative to model selection.
For those interested repo is here: https://github.com/shubchat/loab
Happy to share more details if there's interest. Curious if anyone is working on similar evaluation methodology problems in other domains.
#technology
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