Successfully Guiding Humans with Imperfect Instructions by Highlighting Potential Errors and Suggesting Corrections Article Swipe
Lingjun Zhao
,
Khanh Nguyen
,
Hal Daumé
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.16973
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.16973
Language models will inevitably err in situations with which they are unfamiliar. However, by effectively communicating uncertainties, they can still guide humans toward making sound decisions in those contexts. We demonstrate this idea by developing HEAR, a system that can successfully guide humans in simulated residential environments despite generating potentially inaccurate instructions. Diverging from systems that provide users with only the instructions they generate, HEAR warns users of potential errors in its instructions and suggests corrections. This rich uncertainty information effectively prevents misguidance and reduces the search space for users. Evaluation with 80 users shows that HEAR achieves a 13% increase in success rate and a 29% reduction in final location error distance compared to only presenting instructions to users. Interestingly, we find that offering users possibilities to explore, HEAR motivates them to make more attempts at the task, ultimately leading to a higher success rate. To our best knowledge, this work is the first to show the practical benefits of uncertainty communication in a long-horizon sequential decision-making problem.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.16973
- https://arxiv.org/pdf/2402.16973
- OA Status
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4392270653Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2402.16973Digital Object Identifier
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Successfully Guiding Humans with Imperfect Instructions by Highlighting Potential Errors and Suggesting CorrectionsWork title
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-02-26Full publication date if available
- Authors
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Lingjun Zhao, Khanh Nguyen, Hal DauméList of authors in order
- Landing page
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https://arxiv.org/abs/2402.16973Publisher landing page
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https://arxiv.org/pdf/2402.16973Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2402.16973Direct OA link when available
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Imperfect, Computer science, Psychology, Cognitive psychology, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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