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---
title: OmniEval
emoji: 🥇
colorFrom: green
colorTo: indigo
sdk: gradio
app_file: app.py
pinned: true
license: apache-2.0
short_description: Official Leaderboard for OmniEval
---
---
license: mit
language:
- zh
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
---
# Dataset Information
We introduce an omnidirectional and automatic RAG benchmark, **OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain**, in the financial domain. Our benchmark is characterized by its multi-dimensional evaluation framework, including:
1. a matrix-based RAG scenario evaluation system that categorizes queries into five task classes and 16 financial topics, leading to a structured assessment of diverse query scenarios;
2. a multi-dimensional evaluation data generation approach, which combines GPT-4-based automatic generation and human annotation, achieving an 87.47% acceptance ratio in human evaluations on generated instances;
3. a multi-stage evaluation system that evaluates both retrieval and generation performance, result in a comprehensive evaluation on the RAG pipeline;
4. robust evaluation metrics derived from rule-based and LLM-based ones, enhancing the reliability of assessments through manual annotations and supervised fine-tuning of an LLM evaluator.
Useful Links: 📝 [Paper](https://arxiv.org/abs/2412.13018) • 🤗 [Hugging Face](https://huggingface.co/collections/RUC-NLPIR/omnieval-67629ccbadd3a715a080fd25) • 🧩 [Github](https://github.com/RUC-NLPIR/OmniEval)
We have trained two models from Qwen2.5-7B by the lora strategy and human-annotation labels to implement model-based evaluation.Note that the evaluator of hallucination is different from other four.
We provide the evaluator for other metrics except hallucination in this repo.
# 🌟 Citation
```bibtex
@misc{wang2024omnievalomnidirectionalautomaticrag,
title={OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain},
author={Shuting Wang and Jiejun Tan and Zhicheng Dou and Ji-Rong Wen},
year={2024},
eprint={2412.13018},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.13018},
}
```
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