We propose a novel approach to conformal prediction for generative language
models (LMs). Standard conformal prediction produces prediction sets -- in
place of single predictions -- that have rigorous, statistical performance
guarantees. LM responses are typically sampled from the model's predicted
distribution over the large, combinatorial output space of natural language.
Translating this process to conformal prediction, we calibrate a stopping rule
for sampling different outputs from the LM that get added to a growing set of
candidates until we are confident that the output set is sufficient. Since some
samples may be low-quality, we also simultaneously calibrate and apply a
rejection rule for removing candidates from the output set to reduce noise.
Similar to conformal prediction, we prove that the sampled set returned by our
procedure contains at least one acceptable answer with high probability, while
still being empirically precise (i.e., small) on average. Furthermore, within
this set of candidate responses, we show that we can also accurately identify
subsets of individual components -- such as phrases or sentences -- that are
each independently correct (e.g., that are not "hallucinations"), again with
statistical guarantees. We demonstrate the promise of our approach on multiple
tasks in open-domain question answering, text summarization, and radiology
report generation using different LM variants.

该研究介绍了一种新的适用于生成式语言模型的公差预测方法，它不仅能够精确地预测结果，还具有统计学的可靠性。研究者们通过校准样本输出和严格限制机制来实现这种方法。