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Cited by 11Explainable Artificial Intelligence (XAI)Usability and User Interface DesignNatural Language Processing Techniques

Follow the Successful Herd: Towards Explanations for Improved Use and Mental Models of Natural Language Systems

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Brachman, Michelle, Pan, Qian, Do, Hyo Jin, Dugan, Casey, Chaudhary, Arunima, Johnson, James M., Rai, Priyanshu, Chakraborti, Tathagata +6 more·2023·Proceedings of the 28th International Conference on Intelligent User Interfaces
DOI 10.1145/3581641.3584088
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Abstract

While natural language systems continue improving, they are still imperfect. If a user has a better understanding of how a system works, they may be able to better accomplish their goals even in imperfect systems. We explored whether explanations can support effective authoring of natural language utterances and how those explanations impact users’ mental models in the context of a natural language system that generates small programs. Through an online study (n=252), we compared two main types of explanations: 1) system-focused, which provide information about how the system processes utterances and matches terms to a knowledge base, and 2) social, which provide information about how other users have successfully interacted with the system. Our results indicate that providing social suggestions of terms to add to an utterance helped users to repair and generate correct flows more than system-focused explanations or social recommendations of words to modify. We also found that participants commonly understood some mechanisms of the natural language system, such as the matching of terms to a knowledge base, but they often lacked other critical knowledge, such as how the system handled structuring and ordering. Based on these findings, we make design recommendations for supporting interactions with and understanding of natural language systems.

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  • Explanations that map each aspect of an output to aspects of the prompt and compare this to examples of effective prompts can help users disentangle issues with their prompt from those stemming from model performance, thereby supporting confidence adjustment for prompting ability.

    “For example, explanations that map each aspect of an output to aspects of the prompt (e.g., using attention visualization [179]), and compare this to examples of effective prompts [25,85], can help users disentangle issues with their prompt from those stemming from model performance, thereby supporting confidence adjustment for prompting ability.”

    From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [25]

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Authors
Brachman, M., Pan, Q., Do, H. J., Dugan, C., Chaudhary, A., Johnson, J. M., Rai, P., Chakraborti, T., Gschwind, T., Laredo, J. A., Miksovic, C., Scotton, P., Talamadupula, K., & Thomas, G.
Affiliations
  • IBM (United States) (US)
  • LinkedIn (United States) (US)
  • IBM Research - India (IN)
  • IBM Research - Zurich (CH)
ORCID
Venue
Proceedings of the 28th International Conference on Intelligent User Interfaces
Publisher
ACM
Published
2023-03-27
Pages
220-239
DOI
10.1145/3581641.3584088
OpenAlex
W4360991186
Cited by
11
References
27
Citations by year
2026: 1 · 2025: 4 · 2024: 6
FWCI
1.95
Type
conference paper
Language
en
Open access
Licence
Locations
ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
natural language interfaces, explanation, mental models, social recommendation, natural language programming

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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