  Natural language reasoning plays an increasingly important role in improving
language models' ability to solve complex language understanding tasks. An
interesting use case for reasoning is the resolution of context-dependent
ambiguity. But no resources exist to evaluate how well Large Language Models
can use explicit reasoning to resolve ambiguity in language. We propose to use
ambiguous definite descriptions for this purpose and create and publish the
first benchmark dataset consisting of such phrases. Our method includes all
information required to resolve the ambiguity in the prompt, which means a
model does not require anything but reasoning to do well. We find this to be a
challenging task for recent LLMs. Code and data available at:
https://github.com/sfschouten/exploiting-ambiguity
