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Toolkit for "TrustLLM: Trustworthiness in Large Language Models"

Updates & News

• [01/09/2024] TrustLLM toolkit has been downloaded for 4000+ times!
• [15/07/2024] TrustLLM now supports UniGen for dynamic evaluation.
• [02/05/2024] 🥂 TrustLLM has been accepted by ICML 2024! See you in Vienna!
• [23/04/2024] :star: Version 0.3.0: Major updates including bug fixes, enhanced evaluation, and new models added (including ChatGLM3, Llama3-8b, Llama3-70b, GLM4, Mixtral). (See details)
• [20/03/2024] :star: Version 0.2.4: Fixed many bugs & Support Gemini Pro API
• [01/02/2024] :page_facing_up: Version 0.2.2: See our new paper about the awareness in LLMs! (link)
• [29/01/2024] :star: Version 0.2.1: trustllm toolkit now supports (1) Easy evaluation pipeline (2) LLMs in replicate and deepinfra (3) Azure OpenAI API
• [20/01/2024] :star: Version 0.2.0 of trustllm toolkit is released! See the new features.
• [12/01/2024] :surfer: The dataset, leaderboard, and evaluation toolkit are released!

👂TL;DR

• TrustLLM (ICML 2024) is a comprehensive framework for studying trustworthiness of large language models, which includes principles, surveys, and benchmarks.
• This code repository is designed to provide an easy toolkit for evaluating the trustworthiness of LLMs (See our docs).

Table of Content

• About TrustLLM
• Before Evaluation
  • Installation
  • Dataset Download
  • Generation
• Evaluation
• Dataset & Task
  • Dataset overview:
  • Task overview:
• Leaderboard
• Contribution
• Citation
• License

🙋 About TrustLLM

We introduce TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

🧹 Before Evaluation

Installation

Installation via Github (recommended):

git clone git@github.com:HowieHwong/TrustLLM.git

Installation via pip:

pip install trustllm

Installation via conda:

conda install -c conda-forge trustllm

Create a new environment:

conda create --name trustllm python=3.9

Install required packages:

cd trustllm_pkg
pip install .

Dataset Download

Download TrustLLM dataset:

from trustllm.dataset_download import download_dataset

download_dataset(save_path='save_path')

Generation

We have added generation section from version 0.2.0. Start your generation from this page. Here is an example:

from trustllm.generation.generation import LLMGeneration

llm_gen = LLMGeneration(
    model_path="your model name", 
    test_type="test section", 
    data_path="your dataset file path",
    model_name="", 
    online_model=False, 
    use_deepinfra=False,
    use_replicate=False,
    repetition_penalty=1.0,
    num_gpus=1, 
    max_new_tokens=512, 
    debug=False,
    device='cuda:0'
)

llm_gen.generation_results()

🙌 Evaluation

We have provided a toolkit that allows you to more conveniently assess the trustworthiness of large language models. Please refer to the document for more details. Here is an example:

from trustllm.task.pipeline import run_truthfulness

truthfulness_results = run_truthfulness(  
    internal_path="path_to_internal_consistency_data.json",  
    external_path="path_to_external_consistency_data.json",  
    hallucination_path="path_to_hallucination_data.json",  
    sycophancy_path="path_to_sycophancy_data.json",
    advfact_path="path_to_advfact_data.json"
)

🛎️ Dataset & Task

Dataset overview:

✓ the dataset is from prior work, and ✗ means the dataset is first proposed in our benchmark.

Task overview:

○ means evaluation through the automatic scripts (e.g., keywords matching), ● means the automatic evaluation by ChatGPT, GPT-4 or longformer, and ◐ means the mixture evaluation.

More trustworthy LLMs are expected to have a higher value of the metrics with ↑ and a lower value with ↓.

🏆 Leaderboard

If you want to view the performance of all models or upload the performance of your LLM, please refer to this link.

📣 Contribution

We welcome your contributions, including but not limited to the following:

• New evaluation datasets
• Research on trustworthy issues
• Improvements to the toolkit

If you intend to make improvements to the toolkit, please fork the repository first, make the relevant modifications to the code, and finally initiate a pull request.

⏰ TODO in Coming Versions

• Faster and simpler evaluation pipeline (Version 0.2.1)
• Dynamic dataset (UniGen)
• More fine-grained datasets
• Chinese output evaluation
• Downstream application evaluation

Citation

@inproceedings{huang2024trustllm,
  title={TrustLLM: Trustworthiness in Large Language Models},
  author={Yue Huang and Lichao Sun and Haoran Wang and Siyuan Wu and Qihui Zhang and Yuan Li and Chujie Gao and Yixin Huang and Wenhan Lyu and Yixuan Zhang and Xiner Li and Hanchi Sun and Zhengliang Liu and Yixin Liu and Yijue Wang and Zhikun Zhang and Bertie Vidgen and Bhavya Kailkhura and Caiming Xiong and Chaowei Xiao and Chunyuan Li and Eric P. Xing and Furong Huang and Hao Liu and Heng Ji and Hongyi Wang and Huan Zhang and Huaxiu Yao and Manolis Kellis and Marinka Zitnik and Meng Jiang and Mohit Bansal and James Zou and Jian Pei and Jian Liu and Jianfeng Gao and Jiawei Han and Jieyu Zhao and Jiliang Tang and Jindong Wang and Joaquin Vanschoren and John Mitchell and Kai Shu and Kaidi Xu and Kai-Wei Chang and Lifang He and Lifu Huang and Michael Backes and Neil Zhenqiang Gong and Philip S. Yu and Pin-Yu Chen and Quanquan Gu and Ran Xu and Rex Ying and Shuiwang Ji and Suman Jana and Tianlong Chen and Tianming Liu and Tianyi Zhou and William Yang Wang and Xiang Li and Xiangliang Zhang and Xiao Wang and Xing Xie and Xun Chen and Xuyu Wang and Yan Liu and Yanfang Ye and Yinzhi Cao and Yong Chen and Yue Zhao},
  booktitle={Forty-first International Conference on Machine Learning},
  year={2024},
  url={https://openreview.net/forum?id=bWUU0LwwMp}
}

License

The code in this repository is open source under the MIT license.