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Evidence record 433 · automatically gathered

Test-Time Verification for Text-to-SQL via Outcome Reward Models

Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL. Common test-time inference strategies, including Best-of-N sampling and Majority Voting, rely on heuristic signals such as execution success or output frequency, which provide limited semantic discrimination across candidate outputs. In this work, we study Outcome Reward Models (ORMs) as learned semantic scoring functions for test-time verification

Record details

Published: 29 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (29 June 2026), “Test-Time Verification for Text-to-SQL via Outcome Reward Models,” evidence record 433, https://ethics.ai/record/433 (originally published by arXiv).

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