🔍 xVerify-7B-I

xVerify is an evaluation tool fine-tuned from a pre-trained large language model, designed specifically for objective questions with a single correct answer. It is presented in the paper xVerify: Efficient Answer Verifier for Reasoning Model Evaluations.

It accurately extracts the final answer from lengthy reasoning processes and efficiently identifies equivalence across different forms of expressions.

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✨ Key Features

📊 Broad Applicability

Suitable for various objective question evaluation scenarios including math problems, multiple-choice questions, classification tasks, and short-answer questions.

⛓️ Handles Long Reasoning Chains

Effectively processes answers with extensive reasoning steps to extract the final answer, regardless of complexity.

🌐 Multilingual Support

Primarily handles Chinese and English responses while remaining compatible with other languages.

🔄 Powerful Equivalence Judgment

• ✓ Recognizes basic transformations like letter case changes and Greek letter conversions
• ✓ Identifies equivalent mathematical expressions across formats (LaTeX, fractions, scientific notation)
• ✓ Determines semantic equivalence in natural language answers
• ✓ Matches multiple-choice responses by content rather than just option identifiers

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🚀 Sample Usage

This snippet demonstrates single-sample evaluation using the Evaluator logic provided in the official repository.

from src.xVerify.model import Model
from src.xVerify.eval import Evaluator

# initialization
model_name = 'xVerify-7B-I'
model_path = 'IAAR-Shanghai/xVerify-7B-I'
inference_mode = 'local' 

model = Model(
    model_name=model_name,
    model_path_or_url=model_path,
    inference_mode=inference_mode,
)
evaluator = Evaluator(model=model)

# input evaluation information
question = "New steel giant includes Lackawanna site A major change is coming to the global steel industry and a galvanized mill in Lackawanna that formerly belonged to Bethlehem Steel Corp.
Classify the topic of the above sentence as World, Sports, Business, or Sci/Tech."
llm_output = "The answer is Business."
correct_answer = "Business"

# evaluation
result = evaluator.single_evaluate(
    question=question,
    llm_output=llm_output,
    correct_answer=correct_answer
)
print(result)

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📚 Citation

@article{xVerify,
      title={xVerify: Efficient Answer Verifier for Reasoning Model Evaluations}, 
      author={Ding Chen and Qingchen Yu and Pengyuan Wang and Wentao Zhang and Bo Tang and Feiyu Xiong and Xinchi Li and Minchuan Yang and Zhiyu Li},
      journal={arXiv preprint arXiv:2504.10481},
      year={2025},
}