SOURCE-LINKED INTELLIGENCE

TrustDABench: Benchmarking Reliability and Robustness of LLMs for Structured Data Analysis

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

LLMs are increasingly used to analyze spreadsheets, CSV files, and other structured data, but producing a correct-looking answer is not the same as producing a trustworthy analysis. A trustworthy result should be supported by a valid path from the user question to the relevant data evidence. This requirement creates two diagnostic questions: whether an LLM can refuse to answer or ask for clarification when such a path does not exist, and whether it can preserve the correct analysis when the same evidence is expressed in different table forms. We introduce TrustDABench, a benchmark that operati

Read original source ↗ Open in workspace

recordType

paper

region

Global