LGMT is a logic-grounded metamorphic testing framework that detects hidden reasoning defects in LLMs by checking consistency on semantically invariant inputs derived from FOL equivalences.
Hallucinationdetectionfor llm-based text-to-sql generation via two-stage metamorphic testing
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Introduces functional equivalence methods and functional entropy to predict functional correctness of LLM-generated code via uncertainty quantification, outperforming NLI-based baselines in most tested settings.
A paired benchmark demonstrates that providing an explicit semantic layer document improves LLM accuracy on text-to-SQL tasks by 17-23 percentage points and eliminates meaningful differences between frontier models.
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LGMT: Logic-Grounded Metamorphic Testing for Evaluating the Reasoning Reliability of LLMs
LGMT is a logic-grounded metamorphic testing framework that detects hidden reasoning defects in LLMs by checking consistency on semantically invariant inputs derived from FOL equivalences.
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Semantic Layers for Reliable LLM-Powered Data Analytics: A Paired Benchmark of Accuracy and Hallucination Across Three Frontier Models
A paired benchmark demonstrates that providing an explicit semantic layer document improves LLM accuracy on text-to-SQL tasks by 17-23 percentage points and eliminates meaningful differences between frontier models.