LangTest is an open-source testing framework whose goal is to help developers deliver safe and effective language models. It is aimed at data scientists and NLP engineers, providing systematic model quality assessment capabilities to make up for the current lack of tool support for NLP models in production environments in terms of safety, robustness, and fairness.
Main Capabilities
LangTest can generate and execute more than 60 different types of tests with just one line of code. The scope of testing covers multiple aspects of model quality: robustness, bias, representation, fairness, and accuracy. In addition, it supports automatically augmenting training data based on test results, but this feature applies only to some selected models.
Supported Frameworks and Tasks
In terms of traditional NLP frameworks, LangTest supports Spark NLP, Hugging Face, and Transformers for named entity recognition (NER), translation, and text classification tasks.
In terms of large language models, LangTest supports testing LLMs provided by OpenAI, Cohere, AI21, Hugging Face Inference API, and Azure-OpenAI. Test scenarios include question answering, toxicity, clinical testing, legal support, factuality, sycophancy, summarization, and other common test types.
Benchmark Datasets
LangTest comes with a variety of datasets for testing models, covering a wide range of use cases and evaluation scenarios. The documentation mentions that you can explore all available benchmark datasets, each of which has been carefully curated to challenge and improve language models. Whether you focus on tasks such as question answering or text summarization, LangTest provides corresponding data to test model performance across diverse language tasks.
Usage
According to official materials, using LangTest allows you to generate and execute tests with one line of code. For more extended usage and documentation, see langtest.org. The materials do not provide specific installation commands or code examples.
Community and Support
The project provides multiple community support channels: there is a #langtest channel on Slack for real-time discussion; GitHub for bug reports, feature requests, and contributions; and Discussions for community members to exchange ideas and share usage experience. The project welcomes all kinds of contributions, with detailed instructions in the contribution guidelines. Contributors must abide by the project's code of conduct. In addition, the project has published a citable paper.
License
LangTest is released under the Apache License 2.0, which guarantees commercial use, modification, distribution, patent use, and private use, and sets limitations on trademark use, liability, and warranty.
Project Background and Maintenance
The project has been assigned a complete development team by John Snow Labs, and it is committed to continuously improving the library over the long term just like maintaining other open-source libraries. Frequent new releases are expected, with new test types, tasks, languages, and platforms added regularly. The project is positioned as an early-stage open-source community project, aiming to fill the gap in tools for training safe, robust, and fair AI models, and is built on the foundation of multiple previous research efforts.
