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

We study whether language models can evaluate the validity of their own
claims and predict which questions they will be able to answer correctly. We
first show that larger models are well-calibrated on diverse multiple choice
and true/false questions when they are provided in the right format. Thus we
can approach self-evaluation on open-ended sampling tasks by asking models to
first propose answers, and then to evaluate the probability "P(True)" that
their answers are correct. We find encouraging performance, calibration, and
scaling for P(True) on a diverse array of tasks. Performance at self-evaluation
further improves when we allow models to consider many of their own samples
before predicting the validity of one specific possibility. Next, we
investigate whether models can be trained to predict "P(IK)", the probability
that "I know" the answer to a question, without reference to any particular
proposed answer. Models perform well at predicting P(IK) and partially
generalize across tasks, though they struggle with calibration of P(IK) on new
tasks. The predicted P(IK) probabilities also increase appropriately in the
presence of relevant source materials in the context, and in the presence of
hints towards the solution of mathematical word problems. We hope these
observations lay the groundwork for training more honest models, and for
investigating how honesty generalizes to cases where models are trained on
objectives other than the imitation of human writing.

本研究旨在探究自然语言模型是否可以自我评估其答案准确性并预测正确答案概率，结果表明较大模型能够准确预测答案概率，训练可提高模型预测 P（IK）的能力，但模型在新任务上仍存在校准困难。

语言模型（基本）知道它们知道的

Language Models (Mostly) Know What They Know

In many real-world applications of machine learning classifiers, it is
essential to predict the probability of an example belonging to a particular
class. This paper proposes a simple technique for predicting probabilities
based on optimizing a ranking loss, followed by isotonic regression. This
semi-parametric technique offers both good ranking and regression performance,
and models a richer set of probability distributions than statistical
workhorses such as logistic regression. We provide experimental results that
show the effectiveness of this technique on real-world applications of
probability prediction.

本文提出了一种使用排名损失函数和等变回归来优化概率预测的技术，具有良好的排名和回归性能，并且在概率分布上比逻辑回归等统计学习方法表现更好，证明了该技术在实际应用中的有效性。