unieval fact

Introduction

Multi-dimensional evaluation is prevalent for human assessment in Natural Language Generation (NLG), focusing on dimensions like coherence and fluency. However, automatic evaluations still rely heavily on similarity-based metrics (e.g., ROUGE, BLEU), which are inadequate for distinguishing advanced generation models. UniEval addresses this gap, providing a more comprehensive and fine-grained evaluation of NLG systems.

Architecture

UniEval is designed as a multi-dimensional evaluator that can assess various aspects of text generation, with a focus on factual consistency. It is specifically pre-trained for the task of factual consistency detection, allowing it to evaluate model outputs and predict consistency scores effectively.

Training

The UniEval model is pre-trained to evaluate factual consistency in text generation tasks. Specific training details are available in the linked GitHub repository.

Guide: Running Locally

To run UniEval locally, follow these basic steps:

1. Clone the Repository: Clone the GitHub repository to your local machine.
2. Install Dependencies: Ensure you have all necessary dependencies installed. This typically involves installing libraries like PyTorch and Transformers.
3. Download the Model: Use the repository instructions to download the pre-trained UniEval model.
4. Run Evaluations: Use the provided scripts to evaluate text generation outputs with UniEval.

For optimal performance, consider using cloud GPUs such as those available on AWS, Google Cloud, or Azure.

License

Refer to the GitHub repository for detailed licensing information regarding the use of UniEval.