README.md

metadata

license: mit
language:
  - en
tags:
  - text-rewriting
  - web
  - generative-engine-optimization
  - geo
  - reinforcement-learning
  - grpo
  - qwen3
  - transformers
  - safetensors
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen3-1.7B
datasets:
  - cx-cmu/GEO-Bench

AutoGEO_Mini (Qwen1.7B, GEO-Bench)

AutoGEO_Mini (Qwen1.7B, GEO-Bench) is a GEO model designed to improve how web document is incorporated into answers generated by LLM-based generative engines.

The model rewrites a given document to better match the preferences of generative engines (e.g., GPT, Gemini, Claude), with the goal of increasing the document’s visibility and coverage in generated responses, while preserving the original meaning and factual content.

⚠️ This model is trained for the generative engine powered by gemini-2.5-flash-lite on dataset GEO-Bench. If you intend to use AutoGEO_Mini with other types of generative engines or datasets, you must post-train Qwen/Qwen3-1.7B using our code.

This model is part of the AutoGEO framework proposed in the paper

📄 Paper: "What Generative Search Engines Like and How to Optimize Web Content Cooperatively"
👥 Authors: Yujiang Wu*, Shanshan Zhong*, Yubin Kim, Chenyan Xiong (*Equal contribution)
🚀 Code: AutoGEO on GitHub

Usage

This model is designed to be used through the AutoGEO framework. Try it out in huggingface Space or

Quick starts:

from autogeo.rewriters import rewrite_document

rewritten_text = rewrite_document(
    document="Input text.",
    dataset="GEO-Bench",
    engine_llm="gemini",
    model_path="cx-cmu/AutoGEO_mini_Qwen1.7B_ResearchyGEO",
)

Evaluation:

python -m autogeo.evaluate \
  --model autogeo_mini \
  --model_path cx-cmu/AutoGEO_mini_Qwen1.7B_ResearchyGEO \
  --dataset GEO-Bench

Related Resources

• Paper: https://arxiv.org/abs/2510.11438
• Code: https://github.com/cxcscmu/AutoGEO
• Dataset: https://huggingface.co/datasets/cx-cmu/GEO-Bench

Citation

If you use this model, please cite:

@article{wu2025generative,
  title={What Generative Search Engines Like and How to Optimize Web Content Cooperatively},
  author={Wu, Yujiang and Zhong, Shanshan and Kim, Yubin and Xiong, Chenyan},
  journal={arXiv preprint arXiv:2510.11438},
  year={2025}
}