A-VERT: Agnostic Verification with Embedding Ranking Targets

• URL: http://arxiv.org/abs/2510.01469v1
• Date: Wed, 01 Oct 2025 21:26:03 GMT
• Title: A-VERT: Agnostic Verification with Embedding Ranking Targets
• Authors: Nicolás Aguirre, Ramiro Caso, Ramiro Rodríguez Colmeiro, Mauro Santelli, Joaquín Toranzo Calderón,
• Abstract summary: A structure-free evaluation method is presented.<n>The method makes use of semantic embedding distances to match target candidates with arbitrary LM-generated text.<n>The results show a regression score of 0.97 and an accuracy of 96% against human annotators.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automatic evaluation of Language Model (LM) responses is a critical piece in the development of benchmarks and metrics, both for model training and quality assessment of production model endpoints. The current approaches to response classification relies on methods that are too expensive (i.e. LLM-as-a-Judge) or that are far from real-world conditions (string-matching, logprob). In this paper, a structure-free evaluation method is presented. The method makes use of semantic embedding distances to match target candidates with arbitrary LM-generated text, resulting in a robust classification of the response at a relatively low compute cost (embedding models of less than $10B$ parameters). The results show a regression score of ~0.97 and an accuracy of ~96% against human annotators, tested over 3 data sets and 3 different LM architectures.

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