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Explaining machine learning models with interactive natural language conversations using TalkToModel
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Abstract
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability methods because they do not know which explanation to choose and how to interpret the explanation. Here we address the challenge of using explainability methods by proposing TalkToModel: an interactive dialogue system that explains ML models through natural language conversations. TalkToModel consists of three components: an adaptive dialogue engine that interprets natural language and generates meaningful responses; an execution component that constructs the explanations used in the conversation; and a conversational interface. In real-world evaluations, 73% of healthcare workers agreed they would use TalkToModel over existing systems for understanding a disease prediction model, and 85% of ML professionals agreed TalkToModel was easier to use, demonstrating that TalkToModel is highly effective for model explainability.
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“The few studies that move beyond purely local explanations [11, 74,80,81,104,116,125,129] point to promising but underexplored directions.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [104]
In the citing paper
“Of the 69 reviewed studies, only three [75,76,104] included any user-facing evaluation of explanation presentation.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [104]
In the citing paper
“A minority of studies engaged clinicians to evaluate explanations [67,83,87,102,104], while most relied entirely on automated metrics or proxies.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [104]
In the citing paper
“Only one [104] conducted a controlled user study comparing explanation formats.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [104]
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Metadata
- Authors
- Slack, D., Krishna, S., Lakkaraju, H., & Singh, S.
- Affiliations
- University of California, Irvine (US)
- Harvard University (US)
- Corresponding author
- Slack, Dylan
- Venue
- Nature Machine Intelligence
- Published
- 2023-07-27
- Volume
- 5
- Issue
- 8
- Pages
- 873-883
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W4385302687
- Cited by
- 125
- References
- 80
- Citations by year
- 2026: 31 · 2025: 53 · 2024: 33 · 2023: 8
- FWCI
- 11.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Machine Intelligence (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- Funders
- NSF | Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems (IIS-2040989, IIS-2046873, IIS-2008956, IIS-2008461)
- I was supported by a fellowship from the hasso plattner institute during the bulk of completing this work.
- Google, JP Morgan, Amazon, Harvard Data Science Initiative, D^3 institute at Harvard
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- machine learning explainability, natural language dialogue systems, interactive explanations, conversational interfaces, model understanding, disease prediction model, healthcare workers
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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