LatentRefusal: Latent-Signal Refusal for Unanswerable Text-to-SQL Queries

Researchers propose LatentRefusal, a safety mechanism for LLM-based text-to-SQL systems that detects unanswerable queries by analyzing intermediate hidden activations rather than relying on output-level instruction following. The approach achieves 88.5% F1 score across four benchmarks while adding minimal computational overhead, addressing a critical deployment challenge in AI systems that generate executable code.

LatentRefusal addresses a fundamental safety problem in deploying large language models for database querying: detecting when user queries cannot be safely answered before the system generates misleading or unsafe SQL code. Traditional refusal mechanisms either depend on explicit model instructions—which fail due to hallucinations—or estimate uncertainty through computationally expensive methods. This research introduces a novel approach by treating answerability detection as a latent-signal prediction problem, analyzing the model's internal representations rather than its outputs.

The technical contribution centers on the Tri-Residual Gated Encoder, a lightweight probing architecture designed to identify sparse, localized signals within a model's hidden layers that indicate question-schema mismatches. By working at the latent level, this method sidesteps the brittleness of instruction-following approaches and avoids the complexity of uncertainty estimation. The mechanism acts as an attachable safety layer, suggesting practical deployment compatibility.

For the broader AI safety and LLM deployment landscape, this work represents incremental but meaningful progress toward safer code-generation systems. Text-to-SQL applications have real consequences—incorrect queries affect data integrity and system reliability—making robust refusal mechanisms essential for enterprise adoption. The 88.5% F1 improvement across diverse benchmarks indicates the approach generalizes across different failure modes.

Looking ahead, the focus should be on whether this technique extends beyond text-to-SQL to other code-generation tasks and how it performs against adversarial inputs specifically designed to bypass detection. Integration into production systems and comparison with alternative safety approaches would further validate practical utility.

• →LatentRefusal detects unanswerable queries by analyzing hidden layer activations rather than output-level instruction following, improving robustness against hallucinations.
• →The Tri-Residual Gated Encoder achieves 88.5% F1 score while adding only ~2 milliseconds of computational overhead per query.
• →This mechanism provides an attachable safety layer for text-to-SQL systems, enabling safer deployment in production environments.
• →The approach addresses a critical gap in LLM-based code generation by preventing execution of misleading or unsafe SQL queries.
• →Latent-signal refusal represents a shift toward internally-focused safety mechanisms rather than relying on model outputs or external uncertainty estimation.