arXiv:2606.30851v1 Announce Type: new Abstract: 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 in Text-to-SQL. While ORMs have been previously exp
The increasing deployment of large language models in critical applications necessitates robust verification methods to ensure reliability, particularly for structured reasoning tasks.
Improving the reliability of LLM outputs, especially in domains like Text-to-SQL, significantly enhances the trustworthiness and utility of AI systems for enterprise adoption.
This advancement shifts from heuristic-based verification to learned semantic scoring functions, offering a more nuanced and potentially more accurate method for vetting AI outputs.
- · AI developers
- · Enterprises adopting LLMs
- · Data analytics platforms
- · Manual data verification processes
- · LLM applications without robust verification
Increased reliability of LLM applications for complex and structured data interactions.
Faster adoption of AI agents in critical business functions due to enhanced trust in their outputs.
New classes of 'AI auditor' tools and services emerging to specialize in semantic verification and outcome reward model development.
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Read at arXiv cs.CL