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xVerify: Accurate, Efficient LLM Answer Verifier for Reasoning Model Evaluation
- xVerify introduces an efficient answer verifier tailored for evaluating reasoning model responses on objective questions, overcoming challenges in extracting final answers and ensuring answer equivalence across formats.
- The evaluation task is formalized as a 4-tuple (Q,R,Aref,E), emphasizing the extraction of candidate answers and equivalence comparison to reference answers.
- The researchers created the VAR dataset, comprising diverse LLM responses from 19 models across 24 datasets, including multiple question types, prompting strategies, and high-quality annotations.
- Training 14 xVerify models on the VAR dataset demonstrated superior performance across multiple question types, showcasing generalization ability and efficiency compared to existing methods.
- xVerify outperformed rule-based frameworks and judge models in accuracy and cost-effectiveness, with even the smallest model (0.5B parameters) achieving high accuracy and computational efficiency.
- Strong generalization was observed in cases of unseen datasets and models, reinforcing the effectiveness of targeted training and the quality of the VAR dataset.
- The study highlights the importance of specialized evaluation tools like xVerify for assessing reasoning model outputs accurately amidst increasing complexity, setting a precedent for tailored verifiers in complex LLM evaluation tasks.
- By combining innovative data collection, annotation methods, and targeted training, xVerify has emerged as a robust verifier surpassing rule-based frameworks and general-purpose judge models.
- The findings suggest that even smaller parameter models excel at specialized tasks when trained on high-quality datasets, offering computational efficiency and cost-effectiveness in large-scale evaluations.
- xVerify's contributions lie in the creation of the VAR dataset, the development of the xVerify model family, and the demonstration of its superiority in accuracy, generalization ability, computational efficiency, and cost-effectiveness.
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