Risk Assessment and Statistical Significance in the Age of Foundation Models
CoRR(2023)
摘要
We propose a distributional framework for assessing socio-technical risks of
foundation models with quantified statistical significance. Our approach hinges
on a new statistical relative testing based on first and second order
stochastic dominance of real random variables. We show that the second order
statistics in this test are linked to mean-risk models commonly used in
econometrics and mathematical finance to balance risk and utility when choosing
between alternatives. Using this framework, we formally develop a risk-aware
approach for foundation model selection given guardrails quantified by
specified metrics. Inspired by portfolio optimization and selection theory in
mathematical finance, we define a metrics portfolio for each model as a means
to aggregate a collection of metrics, and perform model selection based on the
stochastic dominance of these portfolios. The statistical significance of our
tests is backed theoretically by an asymptotic analysis via central limit
theorems instantiated in practice via a bootstrap variance estimate. We use our
framework to compare various large language models regarding risks related to
drifting from instructions and outputting toxic content.
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关键词
risk assessment,foundation
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