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Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis

arXiv – CS AI|Daniel Gomm, Cornelius Wolff, Madelon Hulsebos|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers propose a new framework for handling ambiguity in natural language queries for tabular data analysis, reframing ambiguity as a cooperative feature rather than a deficiency. The study analyzes 15 datasets and finds that current evaluation methods inadequately assess both system accuracy and interpretation capabilities.

Key Takeaways

→Ambiguity in natural language queries should be viewed as intentional cooperative behavior rather than a system limitation.
→The framework distinguishes between unambiguous, ambiguous cooperative, and uncooperative queries based on shared responsibility between user and system.
→Analysis of 15 datasets reveals uncontrolled mixing of query types that inadequately evaluates natural language interface capabilities.
→Current evaluation methods fail to properly assess both accuracy and interpretation capabilities of tabular data analysis systems.
→The research provides concrete directions for improving design and evaluation of natural language interfaces for data analysis.

#natural-language-processing #tabular-data #query-analysis #human-computer-interaction #evaluation-framework #cooperative-ai #data-analysis #research

Read Original →via arXiv – CS AI

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