Ambiguity is an intrinsic feature of natural language. Managing ambiguity is
a key part of human language understanding, allowing us to anticipate
misunderstanding as communicators and revise our interpretations as listeners.
As language models (LMs) are increasingly employed as dialogue interfaces and
writing aids, handling ambiguous language is critical to their success. We
characterize ambiguity in a sentence by its effect on entailment relations with
another sentence, and collect AmbiEnt, a linguist-annotated benchmark of 1,645
examples with diverse kinds of ambiguity. We design a suite of tests based on
AmbiEnt, presenting the first evaluation of pretrained LMs to recognize
ambiguity and disentangle possible meanings. We find that the task remains
extremely challenging, including for the recent GPT-4, whose generated
disambiguations are considered correct only 32% of the time in human
evaluation, compared to 90% for disambiguations in our dataset. Finally, to
illustrate the value of ambiguity-sensitive tools, we show that a multilabel
NLI model can flag political claims in the wild that are misleading due to
ambiguity. We encourage the field to rediscover the importance of ambiguity for
NLP.

本论文提出了一个基于语言学家注释的语言歧义数据集，并测试了预训练的语言模型在识别和分离歧义上的表现。结果表明，这是一个极具挑战性的任务，现有的模型正确率仅为 32％，本文挑战学术界重新认识语言歧义在自然语言处理中的重要性。同时，本文还展示了一个多标签自然语言推理模型如何帮助识别由于语言歧义而引起的虚假政治言论。