A Correct Answer Is Not the Same as a Verified Analysis
mlj.solutions
mlj.solutions Odpověď AI nad tabulkou není analýza, dokud neumí ukázat zdroj, filtr a výpočet. I špičkové modely v novém benchmarku často pokračovaly bez opory v datech. 🔗Paper: https://arxiv.org/abs/2608.24145
View on InstagramWhat the study is about
Language models now analyze tables and CSV files, but a correct-looking answer is not yet a trustworthy analysis. TrustDABench tests two things: whether a model can refuse to answer or ask a follow-up question when the data isn't sufficient, and whether it keeps the correct result when the same data is written in a different table layout.
Statistics
24.21%
best reliability result
Even the best model, GPT-5.5, didn't reach a quarter in the reliability test. The test measures whether a model refuses to answer or asks a follow-up when the data doesn't allow an answer.
9.10%
best robustness result
The robustness test measures whether a model keeps the correct analysis when the same data is written differently. Claude Sonnet 5 did best, but even it wasn't flawless.
2,340
human-verified cases
The benchmark contains 2,340 modified cases built from 19 types of data modifications and evaluates eight language models.
What this means
- A correct-looking answer isn't enough, a trustworthy analysis must have a documented path from the question to the data evidence.
- Models rarely catch a contradiction, in data with conflicting information they mostly fail to notice it.
- They carry on even without supporting evidence, they often reach a result through a calculation that is feasible but has no basis in the data.
- Table shape matters, the result changes when the observation boundaries or the links between tables change.
- Recognizing the limits of the data is missing, the authors recommend strengthening the recognition of evidence boundaries and reasoning that doesn't depend on how the data is written.
Our take
A correct-looking result is not yet a verified analysis. That's why we set up our AI so that the agent can refuse or ask a follow-up question when the data isn't sufficient for an answer.
Zdroj
Shi, B., Liu, Y., Zhao, C., Chi, C., Wang, Z., Wang, X., & Feng, J. (2026). TrustDABench: Benchmarking Reliability and Robustness of LLMs for Structured Data Analysis. arXiv:2608.24145. https://arxiv.org/abs/2608.24145
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