π xVerify-1B-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 accurately extracts the final answer from lengthy reasoning processes and efficiently identifies equivalence across different forms of expressions.
The model was presented in the paper xVerify: Efficient Answer Verifier for Reasoning Model Evaluations.
β¨ Key Features
π Broad Applicability
Suitable for various objective question evaluation scenarios including math problems, multiple-choice questions, classification tasks, and short-answer questions.
βοΈ Handles Long Reasoning Chains
Effectively processes answers with extensive reasoning steps to extract the final answer, regardless of complexity.
π Multilingual Support
Primarily handles Chinese and English responses while remaining compatible with other languages.
π Powerful Equivalence Judgment
- β Recognizes basic transformations like letter case changes and Greek letter conversions
- β Identifies equivalent mathematical expressions across formats (LaTeX, fractions, scientific notation)
- β Determines semantic equivalence in natural language answers
- β Matches multiple-choice responses by content rather than just option identifiers
π Sample Usage
According to the official repository, you can use the model for evaluation as follows:
# Single sample evaluation test
from src.xVerify.model import Model
from src.xVerify.eval import Evaluator
# initialization
model_name = 'xVerify-1B-I' # Model name
url = 'IAAR-Shanghai/xVerify-1B-I' # Model path or URL
inference_mode = 'local' # Inference mode, 'local' or 'api'
api_key = None # API key used to access the model via API, if not available, set to None
model = Model(
model_name=model_name,
model_path_or_url=url,
inference_mode=inference_mode,
api_key=api_key
)
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)
π 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},
}
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