Benchmark reveals LLMs generate SQL for impossible queries instead of refusing
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
A new benchmark exposes a critical blind spot in production LLM systems: the inability to recognize and refuse flawed user queries. PremiseSQL introduces 1,800 premise-failure test cases across four failure modes, revealing that frontier models often generate plausible but misleading SQL rather than abstaining. This matters because deployed NL2SQL systems currently lack guardrails against logically incoherent requests, creating silent failure modes in data applications. The work signals growing attention to model robustness beyond accuracy metrics, forcing builders to confront the gap between benchmark performance and real-world reliability.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
Modelwire analysis
ExplainerOur AI-generated reading of the wider context and the next developments to watch.
PremiseSQL isolates a failure mode that doesn't show up in standard accuracy metrics: the system generates plausible SQL that *sounds* right but violates the logical premise of the user's question. The benchmark doesn't just measure whether models fail; it measures whether they fail *silently* by confidently returning wrong answers instead of saying 'I can't answer that.'
This joins a cluster of recent work exposing gaps between benchmark performance and real-world reliability. The medical device LLM study from mid-October found that production systems lack consistency even on identical queries. DialFork identified procedural hallucination, where agents pass factuality checks while violating task logic. PremiseSQL adds a third angle: the system can be factually coherent and syntactically correct while answering a question that shouldn't be answerable. All three point to the same problem: current metrics reward plausibility over robustness, and deployed systems lack guardrails to refuse incoherent requests.
If the same frontier models tested here show improvement when explicitly prompted to flag premise failures (rather than generate SQL), that confirms the issue is recoverable through instruction rather than architectural. If abstention rates remain flat across prompt variations, the limitation runs deeper and suggests models lack the reasoning capability to detect logical incoherence in the first place.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.LG
New benchmark exposes procedural hallucination in conversational LLMs
Researchers have identified and formalized a critical failure mode in conversational LLMs that existing benchmarks miss: procedural hallucination, where agents generate fluent, factually sound responses while violating the underlying task logic. DialFork, a new 36K-dialogue benchmark spanning 24 tasks across 12 domains, isolates this failure by systematically corrupting dialogue flows and measuring agent deviation at…
MentionsPremiseSQL · LLMs · Text-to-SQL
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “PremiseSQL: Evaluating Premise Critique and Abstention in Text-to-SQL Systems”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.