Successfully Guiding Humans with Imperfect Instructions by Highlighting
  Potential Errors and Suggesting Corrections
Lingjun Zhao Khanh Nguyen Hal Daumé III
This paper addresses the challenge of leveraging imperfect language models to
guide human decision-making in the context of a grounded navigation task. We
show that an imperfect instruction generation model can be complemented with an
effective communication mechanism to become more successful at guiding humans.
The communication mechanism we build comprises models that can detect potential
hallucinations in instructions and suggest practical alternatives, and an
intuitive interface to present that information to users. We show that this
approach reduces the human navigation error by up to 29% with no additional
cognitive burden. This result underscores the potential of integrating diverse
communication channels into AI systems to compensate for their imperfections
and enhance their utility for humans.