GEM-BENCH

This repository provides a comprehensive benchmark for Generative Engine Marketing (GEM), an emerging field that focuses on monetizing generative AI by seamlessly integrating advertisements into Large Language Model (LLM) responses. Our work addresses the core problem of ad-injected response (AIR) generation and provides a framework for its evaluation.

• Generative Engine Marketing (GEM): A new ecosystem where relevant ads are integrated directly into responses from generative AI assistants, such as LLM-based chatbots.
• Ad-injected Response (AIR) Generation: The process of creating responses that seamlessly include relevant advertisements while maintaining a high-quality user experience and satisfying advertiser objectives.
• GEM-BENCH: The first comprehensive benchmark designed for the generation and evaluation of ad-injected responses.

📋 Table of Contents

• Installation
• Getting Started
• Available Datasets
• Evaluation Methods
• Supported Solutions
• License

🔧 Installation

Prerequisites

• Python 3.12 or higher
• Conda (recommended for environment management)

Setup

# Clone the repository
git clone https://github.com/Generative-Engine-Marketing/GEM-Bench.git
cd GemBench

# Create and activate conda environment
conda create --name GemBench python=3.12
conda activate GemBench

# Install Project
pip install -e .

Environment Configuration

Create a .env file in the root directory with the following variables:

# Please fill in your own API keys here and change the file name to .env
OPENAI_API_KEY="<LLMs API Key>"
BASE_URL="<LLMs Base URL>"

TRANSFORMERS_OFFLINE=1 # Enable offline mode for Hugging Face Transformers
HF_HUB_OFFLINE=1 # Enable offline mode for Hugging Face Hub

# Embedding
EMBEDDING_API_KEY="<Embedding API Key>"
EMBEDDING_BASE_URL="<Embedding Base URL>"

🚀 Getting Started

After setting up your environment and configuration, you can run the main script to reproduce the experiments from our paper.

python paper.py

To modify the evaluation, edit the paper.py file to adjust the data_sets, solutions dictionary, and model_name/judge_model parameters.

Available Datasets

The GEM-BENCH benchmark includes three curated datasets that cover both chatbot and search scenarios. You can find their paths within the paper.py script.

• MT-Human: Based on the humanities questions from the MT-Bench benchmark, this dataset is suitable for ad injection in a multi-turn chatbot scenario.
• LM-Market: Curated from the LMSYS-Chat-1M dataset, it contains real user-LLM conversations focused on marketing-related topics.
• CA-Prod: Simulates the AI overview feature in search engines using commercial advertising data from a search engine.

Evaluation Methods

GEM-BENCH provides a multi-faceted metric ontology for evaluating ad-injected responses, covering both quantitative and qualitative aspects of user satisfaction and engagement. The evaluation logic is located in evaluation/.

• Quantitative Metrics:

  • Response Flow & Coherence: Measure the semantic smoothness and topic consistency of the response.
  • Ad Flow & Coherence: Specifically assess how well the ad sentence integrates with the surrounding text.
  • Injection Rate & Click-Through Rate (CTR): Capture the system's ability to deliver ads and user engagement.

• Qualitative Metrics:

  • User Satisfaction: Evaluated on dimensions like Accuracy, Naturalness (interruptiveness, authenticity), Personality (helpfulness, salesmanship), and Trust (credibility, bias).
  • User Engagement: Measured by Notice (awareness, attitude) and Click (awareness of sponsored links, likelihood to click).

Supported Solutions

The benchmark provides implementations for several baseline solutions, allowing for flexible experimentation. You can find their configurations and exposed parameters within the paper.py file.

• Ad-Chat: An existing solution that integrates ads into the system prompt of the LLM.

  • Parameters: model_name (default: doubao-1-5-lite-32k).

• Ad-LLM: A multi-agent framework inspired by recent work, implemented with different configurations:

  • GI-R: Generate and Inject with ad Retrieval based on the raw response. This is a retrieval-augmented generation (RAG) approach that skips the final rewriting step.
  • GIR-R: Generate, Inject, and Rewrite with ad Retrieval based on the raw response.
  • GIR-P: Generate, Inject, and Rewrite with ad Retrieval based on the user Prompt.
  • Parameters: All Ad-LLM solutions expose the embedding_model and ad_retriever as configurable parameters. The response_rewriter and ad_injector modules also have internal parameters that can be modified.

📖 Citation

If you use GEM-BENCH in your research, please cite our paper:

@article{hu2025gembench,
  title={GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketing},
  author={Hu, Silan and Zhang, Shiqi and Shi, Yimin and Xiao, Xiaokui},
  journal={arXiv preprint arXiv:2509.14221},
  year={2025}
}

For more information, visit our website: https://gem-bench.org

📄 License

This project is licensed under the Apache-2.0 License - see the LICENSE file for details.