# Language Model-Improving Tools Microsoft's AdaTest Leverages LLM Capabilities to Improve Results By Colin Smith | Published 2023-06-15 | Tech Source: Trend Hunter, https://www.trendhunter.com/trends/adatest References: [techcrunch](https://techcrunch.com/2022/05/23/microsoft-claims-its-new-projects-make-language-models-safer-to-use/)  AdaTest is a tool that helps developers find and fix bugs in natural language machine learning models using adaptive testing. It is an interactive process between a user and a language model that results in a tree of unit tests specifically adapted to the model being tested. AdaTest uses language models against themselves to generate test cases that can expose potential flaws in the target model. AdaTest can test any NLP model that can be called with a python function, and it can use any generative language model to help create tests. For example, it can use GPT-3 from OpenAI or GPT-Neo locally. AdaTest also provides a notebook-based testing interface that allows users to create new topics and tests, and view the test results. AdaTEST is an open-source project that was released by [Microsoft Research](https://www.trendhunter.com/trends/llama-2) in May 2022. It aims to enable more reliable and robust large language models by applying [software engineering principles to NLP development](https://www.trendhunter.com/trends/laminar). Image Credit: Microsoft ## Trend Insights (Trend Hunter) - Score: 1.7/10 - Popularity: 19% | Activity: 15% | Freshness: 16% - Audience gender: 50% men, 50% women - Primary generations: Millennial, Gen X - Top markets: North America, Europe, Asia ## Categories [Trend Hunter](https://www.trendhunter.com/trends) > [Tech](https://www.trendhunter.com/tech) > [AI](https://www.trendhunter.com/ai) ## Key Themes ### Key Themes Behind This Trend - **Adaptive Testing:** Using adaptive testing to find and fix bugs in natural language machine learning models. - **NLP Model Testing:** Developing tools specifically designed to test NLP models for potential flaws and weaknesses. - **Software Engineering for NLP:** Applying software engineering principles to NLP development to improve the reliability and robustness of large language models. ### Where This Applies - **Artificial Intelligence:** Leveraging adaptive testing tools to enhance the performance and reliability of AI-powered language models. - **Software Development:** Creating specialized tools for testing and improving the performance of natural language processing models. - **Data Science:** Exploring new methodologies, such as adaptive testing, to enhance the effectiveness and accuracy of NLP algorithms. ## Related on Trend Hunter - [LLM Testing Frameworks](https://www.trendhunter.com/trends/contextcheck.md) - [Code-Verifying AI Tools](https://www.trendhunter.com/trends/testgpt.md) - [AI Agent Testing Solutions](https://www.trendhunter.com/trends/agenttoagent-testing.md) - [Language Model Application Evaluations](https://www.trendhunter.com/trends/llm-evaluation.md) - [GenAI Evaluation Platforms](https://www.trendhunter.com/trends/selene-by-atla.md) - [Natural Language Testing](https://www.trendhunter.com/trends/natural-language-testing.md) - [LLM Evaluation Platforms](https://www.trendhunter.com/trends/confident-ai.md) - [AI Debugging Assistants](https://www.trendhunter.com/trends/latta-ai.md) - [Automated Play Testers](https://www.trendhunter.com/trends/modl-ai.md)