We consider the problem of model multiplicity in downstream decision-making,
a setting where two predictive models of equivalent accuracy cannot agree on
the best-response action for a downstream loss function. We show that even when
the two predictive models approximately agree on their individual predictions
almost everywhere, it is still possible for their induced best-response actions
to differ on a substantial portion of the population. We address this issue by
proposing a framework that calibrates the predictive models with regard to both
the downstream decision-making problem and the individual probability
prediction. Specifically, leveraging tools from multi-calibration, we provide
an algorithm that, at each time-step, first reconciles the differences in
individual probability prediction, then calibrates the updated models such that
they are indistinguishable from the true probability distribution to the
decision-maker. We extend our results to the setting where one does not have
direct access to the true probability distribution and instead relies on a set
of i.i.d data to be the empirical distribution. Finally, we provide a set of
experiments to empirically evaluate our methods: compared to existing work, our
proposed algorithm creates a pair of predictive models with both improved
downstream decision-making losses and agrees on their best-response actions
almost everywhere.

我们提出了一个框架，通过多次校准预测模型以在下游决策问题中与真实概率分布无法区分来解决模型多样性问题，并通过实验验证了改进后的预测模型在减少下游决策损失和达成一致的最优响应行动方面的效果。

调和模型多样性用于下游决策

Reconciling Model Multiplicity for Downstream Decision Making

Language models (LMs) may lead their users to make suboptimal downstream
decisions when they confidently hallucinate. This issue can be mitigated by
having the LM verbally convey the probability that its claims are correct, but
existing models cannot produce text with calibrated confidence statements.
Through the lens of decision-making, we formalize linguistic calibration for
long-form generations: an LM is linguistically calibrated if its generations
enable its users to make calibrated probabilistic predictions. This definition
enables a training framework where a supervised finetuning step bootstraps an
LM to emit long-form generations with confidence statements such as "I estimate
a 30% chance of..." or "I am certain that...", followed by a reinforcement
learning step which rewards generations that enable a user to provide
calibrated answers to related questions. We linguistically calibrate Llama 2 7B
and find in automated and human evaluations of long-form generations that it is
significantly more calibrated than strong finetuned factuality baselines with
comparable accuracy. These findings generalize under distribution shift on
question-answering and under a significant task shift to person biography
generation. Our results demonstrate that long-form generations may be
calibrated end-to-end by constructing an objective in the space of the
predictions that users make in downstream decision-making.

将语言模型从音频转换到文本，并且生成可校准的长篇文本，以便用户能够在后续决策中作出校准的概率预测。