LangBiTe is a framework for testing biases in large language models. Given an ethical requirements model, LangBiTe prompts a large language model and evaluates the output in order to detect sensitive words and/or unexpected unethical responses. It includes a library of prompts to test sexism / misogyny, racism, xenophobia, ageism, political bias, lgtbiq+phobia and religious discrimination. Any contributor may add new ethical concerns to assess.
The following tree shows the list of the repository's sections and their main contents:
└── langbite // The source code of the package.
├── langbite.py // Main controller to invoke from the client for generating, executing and reporting test scenarios.
└── resources
└── prompts_CO_RE.csv // The prompt templates libraries in CSV format.
- python-dotenv 1.0.0
- jsonschema 4.20.0
- openai 1.11.1
- pandas 2.1.4
- replicate 0.23.1
- requests 2.31.0
- numpy 1.26.3
Your project needs the following keys in the .env file:
- API_KEY_OPENAI, to properly connect to OpenAI's API and models.
- API_KEY_HUGGINGFACE, to properly invoke Inference APIs in HuggingFace.
- API_KEY_REPLICATE, to properly connect to models hosted on Replicate.
The following tree shows the contents of the documentation folder:
└── documentation
├── EXTENDING.md
├── USER_GUIDE.md
└── examples
└── input_example.json
Please refer to the USER_GUIDE.md for a description of the input structure and the LangBiTe public methods for generating, executing and reporting the ethical assessment. Within the examples folder you can find an example JSON input with a complete test scenario and its ethical requirements for assessment.
Please refer to EXTENDING.md if you are interested in contributing or populating your own prompt template library, or in connecting additional online LLMs.
The development and community management of this project follows the governance rules described in the GOVERNANCE.md document.
At SOM Research Lab we are dedicated to creating and maintaining welcoming, inclusive, safe, and harassment-free development spaces. Anyone participating will be subject to and agrees to sign on to our CODE_OF_CONDUCT.md.
This project is part of a research line of the SOM Research Lab, but we are open to contributions from the community. Any comment is more than welcome! If you are interested in contributing to this project, please read the CONTRIBUTING.md file.
This repository is the companion to the following research paper (pending publication):
Sergio Morales, Robert Clarisó and Jordi Cabot. "A DSL for Testing LLMs for Fairness and Bias," ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems (MODELS '24), September 22-27, 2024, Linz, Austria
Other publications:
Sergio Morales, Robert Clarisó and Jordi Cabot. "Automating Bias Testing of LLMs," 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), Luxembourg, 2023, pp. 1705-1707, doi: 10.1109/ASE56229.2023.00018 (link)
Sergio Morales, Robert Clarisó and Jordi Cabot. "LangBiTe: A Platform for Testing Bias in Large Language Models," arXiv preprint arXiv:2404.18558 (2024) [cs.SE] (link)
The source code for the site is licensed under the MIT License, which you can find in the LICENSE.md file.