# How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational Behavior

arXiv: https://arxiv.org/abs/2203.08420 · HTML: https://mlsanity.com/paper/2203.08420/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2022.

## Built on by (most cited first, 20 of 20)

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- [DRInQ: Evaluating Conversational Implicature with Controlled Context Variation](https://mlsanity.com/paper/2605.24267/) (2026)
- [Lexara-RF: Reference-Free Metrics for Evaluating Conversational Visual Analytics Agents](https://mlsanity.com/paper/2609.17842/) (2026)

## Abstract (arXiv)

Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interaction. We also draw inspiration from natural language interfaces for data exploration to support ambiguity and intent handling. We ran Wizard of Oz studies with 30 participants to evaluate user expectations for text and voice chatbot design variants. Results identified preferences for intent interpretation and revealed variations in user expectations based on the interface affordances. We subsequently conducted an exploratory analysis of three analytical chatbot systems (text + chart, voice + chart, voice-only) that implement these preferred design variants. Empirical evidence from a second 30-participant study informs implications specific to data-driven conversation such as interpreting intent, data orientation, and establishing trust through appropriate system responses.