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GLEAN: Grounded Lightweight Evaluation Anchors for Contamination-Aware Tabular Reasoning

arXiv – CS AI|Qizhi Wang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers propose GLEAN, a new evaluation protocol for testing small AI models on tabular reasoning tasks while addressing contamination and hardware constraints. The framework reveals distinct error patterns between different models and provides diagnostic tools for more reliable evaluation under limited computational resources.

Key Takeaways

→GLEAN introduces contamination-aware evaluation for tabular reasoning models under 16GB GPU constraints.
→The protocol reveals TAPEX models tend toward grounding errors while TAPAS models show more hallucination issues.
→Framework achieves 95.2% execution rate using SQL anchors for deterministic error classification.
→Retrieval metrics can show high recall even when end-to-end performance remains poor.
→The modular framework includes audits and sensitivity checks for more reliable small-model evaluation.

#ai-evaluation #tabular-reasoning #model-testing #contamination-detection #small-models #benchmark #research #glean

Read Original →via arXiv – CS AI

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