---
license: apache-2.0
datasets:
- PKU-ONELab/NLG-Eval
language:
- en
base_model:
- meta-llama/Meta-Llama-3-8B
---
# Themis

Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability

Paper: https://aclanthology.org/2024.emnlp-main.891

Github: https://github.com/PKU-ONELab/Themis

## Introduction

We propose **Themis**, an 8B-parameter large language model (LLM) specifically designed and trained for NLG evaluation with more comprehensive capabilities. 

Our Themis can evaluate various NLG tasks, including uncommon ones like question-answering evaluation (**Versatility**), in a reference-free manner (**Independence**). Moreover, it allows for specific and customized evaluation aspects and criteria, including overall quality and more fine-grained aspects (**Flexibility**), and its evaluation contains corresponding analysis and explanation together with the rating (**Interpretability**). 

We believe that an ideal evaluator should be convenient to use and possess these characteristics. The comparison between related methods and Themis is shown in the table below.

|      Method       | Versatility | Independence | Flexibility | Interpretability | Open-source |
| :---------------: | :---------: | :----------: | :---------: | :--------------: | :---------: |
|      UniEval      |      ❌      |      ❌       |      ✔️      |        ❌         |      ✔️      |
|      G-Eval       |      ✔️      |      ✔️       |      ✔️      |        ✔️         |      ❌      |
|      X-Eval       |      ✔️      |      ❌       |      ✔️      |        ❌         |      ❌      |
|    Prometheus     |      ✔️      |      ❌       |      ✔️      |        ✔️         |      ✔️      |
|      Auto-J       |      ✔️      |      ✔️       |      ❌      |        ✔️         |      ✔️      |
|   InstructScore   |      ✔️      |      ❌       |      ❌      |        ✔️         |      ✔️      |
|    TIGERScore     |      ✔️      |      ✔️       |      ❌      |        ✔️         |      ✔️      |
| **Themis (Ours)** |      ✔️      |      ✔️       |      ✔️      |        ✔️         |      ✔️      |

## Performance

We implement experiments on several common NLG evaluation tasks and datasets to compare our Themis with other methods, including SummEval for summarization, Topical-Chat for dialogue response generation, SFRES&SFHOT for data-to-text, QAGS for factuality, MANS for story generation, and WMT23 zh-en for machine translation. Experimental results show that our Themis achieves better overall evaluation performance over other evaluation models, including GPT-4.

| Method               | SummEval  | Topical-Chat | SFHOT& SFRES |   QAGS    |   MANS    |   WMT23   | Average Spearman |
| -------------------- | :-------: | :----------: | :---------: | :-------: | :-------: | :-------: | :------------: |
| BLEU                 |   0.075   |    0.388     |    0.024    |     -     |   0.032   |   0.021   |       -        |
| ROUGE                |   0.152   |    0.412     |    0.101    |     -     |  -0.002   |   0.151   |       -        |
| BARTScore            |   0.329   |    0.086     |    0.208    |   0.425   |   0.350   |   0.118   |     0.253      |
| BERTScore            |   0.231   |    0.394     |    0.139    |     -     |   0.285   |   0.219   |       -        |
| BLEURT               |   0.152   |    0.388     |    0.244    |     -     |   0.138   |   0.263   |       -        |
| CometKiwi            |   0.228   |    0.340     |    0.251    |   0.094   |   0.251   |   0.343   |     0.251      |
| UniEval              |   0.474   |    0.577     |    0.282    |     -     |     -     |     -     |       -        |
| G-Eval (GPT-3.5)     |   0.409   |    0.585     |      -      |   0.461   |     -     |     -     |       -        |
| G-Eval (GPT-4)       |   0.523   |    0.588     |      -      |   0.611   |     -     |     -     |       -        |
| GPT-3.5 Turbo        |   0.416   |    0.578     |    0.306    |   0.431   |   0.328   |   0.347   |     0.401      |
| GPT-4 Turbo          |   0.511   |  **0.746**   |    0.320    |   0.637   |   0.473   | **0.437** |     0.521      |
| X-Eval               |   0.480   |    0.605     |    0.303    |   0.578   |     -     |     -     |       -        |
| Prometheus-13B       |   0.163   |    0.434     |    0.173    |     -     |   0.007   |   0.129   |       -        |
| Auto-J-13B           |   0.198   |    0.425     |    0.141    |   0.226   |   0.380   |   0.104   |     0.246      |
| TIGERScore-13B       |   0.384   |    0.346     |    0.200    |   0.504   |   0.231   |   0.248   |     0.319      |
| InstructScore-7B     |   0.258   |    0.241     |    0.247    |     -     |   0.298   |   0.219   |       -        |
| **Themis-8B (ours)** | **0.553** |    0.725     |  **0.333**  | **0.684** | **0.551** |   0.405   |   **0.542**    |

We further conduct more in-depth analyses, including generalization tests on unseen tasks like the instruction-following evaluation as well as aspect-targeted perturbation tests, and our Themis also exhibits superior evaluation performance. For more experimental results and details, please refer to our paper.

## Requirements and Usage

Please refer to our [github repo](https://github.com/PKU-ONELab/Themis) for more details.

## Citation

```
@inproceedings{hu2024themis,
  title={Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability},
  author={Hu, Xinyu and Lin, Li and Gao, Mingqi and Yin, Xunjian and Wan, Xiaojun},
  booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  pages={15924--15951},
  year={2024}
}
```