IAAR-Shanghai/xVerify-3B-Ia

<h1 align="center"> 🔍 xVerify-3B-Ia </h1>

<p align="center"> <div style="display: flex; justify-content: center; gap: 10px;"> <a href="https://github.com/IAAR-Shanghai/xVerify"> <img src="https://img.shields.io/badge/GitHub-Repository-blue?logo=github" alt="GitHub"/> </a> <a href="https://huggingface.co/IAAR-Shanghai/xVerify-3B-Ia"> <img src="https://img.shields.io/badge/🤗%20Hugging%20Face-xVerify--3B--Ia-yellow" alt="Hugging Face"/> </a> <a href="https://huggingface.co/papers/2504.10481"> <img src="https://img.shields.io/badge/Paper-Arxiv-red" alt="Paper"/> </a> </div> </p>

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.

This model was introduced in the paper xVerify: Efficient Answer Verifier for Reasoning Model Evaluations.

✨ 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

🚀 Usage

For detailed instructions on installation and batch evaluation using the xVerify framework, please refer to the official GitHub repository.

Since this is a Llama-based model, you can also use it directly with the transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "IAAR-Shanghai/xVerify-3B-Ia"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

# Your input prompt logic here

📚 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},
}