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from dataclasses import dataclass |
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from enum import Enum |
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@dataclass |
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class Task: |
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benchmark: str |
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metric: str |
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col_name: str |
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class Tasks(Enum): |
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task0 = Task("toxicity", "aggregated-results", "Toxicity") |
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task1 = Task("stereotype", "aggregated-results", "Stereotype") |
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task2 = Task("adv", "aggregated-results", "AdvGLUE++") |
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task3 = Task("ood", "aggregated-results", "OoD") |
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task4 = Task("adv_demo", "aggregated-results", "Adv Demo") |
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task5 = Task("privacy", "aggregated-results", "Privacy") |
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task6 = Task("ethics", "aggregated-results", "Ethics") |
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task7 = Task("fairness", "aggregated-results", "Fairness") |
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TITLE = """<h1 align="center" id="space-title">Trustworthy LLM leaderboard</h1>""" |
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INTRODUCTION_TEXT = """Powered by DecodingTrust, this research endeavor is designed to help researchers better |
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understand the capabilities, limitations, and potential risks associated with deploying these state-of-the-art Large |
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Language Models (LLMs). See our paper for details.""" |
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LLM_BENCHMARKS_TEXT = f""" |
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## How it works |
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DecodingTrust aims at providing a thorough assessment of trustworthiness in GPT models. |
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This research endeavor is designed to help researchers and practitioners better understand the capabilities, |
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limitations, and potential risks involved in deploying these state-of-the-art Large Language Models (LLMs). |
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This project is organized around the following eight primary perspectives of trustworthiness, including: |
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+ Toxicity |
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+ Stereotype and bias |
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+ Adversarial robustness |
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+ Out-of-Distribution Robustness |
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+ Privacy |
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+ Robustness to Adversarial Demonstrations |
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+ Machine Ethics |
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+ Fairness |
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## Reproducibility |
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To reproduce our results, checkout https://github.com/AI-secure/DecodingTrust |
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""" |
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EVALUATION_QUEUE_TEXT = """ |
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## Some good practices before submitting a model |
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### 1) Make sure you can load your model and tokenizer using AutoClasses: |
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```python |
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from transformers import AutoConfig, AutoModel, AutoTokenizer |
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config = AutoConfig.from_pretrained("your model name", revision=revision) |
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model = AutoModel.from_pretrained("your model name", revision=revision) |
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tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision) |
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``` |
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If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded. |
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Note: make sure your model is public! |
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Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted! |
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### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index) |
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It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`! |
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### 3) Make sure your model has an open license! |
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This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗 |
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### 4) Fill up your model card |
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When we add extra information about models to the leaderboard, it will be automatically taken from the model card |
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## In case of model failure |
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If your model is displayed in the `FAILED` category, its execution stopped. |
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Make sure you have followed the above steps first. |
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If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task). |
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""" |
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CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" |
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CITATION_BUTTON_TEXT = r""" |
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@article{wang2023decodingtrust, |
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title={DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models}, |
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author={Wang, Boxin and Chen, Weixin and Pei, Hengzhi and Xie, Chulin and Kang, Mintong and Zhang, Chenhui and Xu, Chejian and Xiong, Zidi and Dutta, Ritik and Schaeffer, Rylan and others}, |
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booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, |
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year={2023} |
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} |
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""" |
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