LangBiTe: A Platform for Testing Bias in Large Language Models
This addresses bias concerns for developers integrating LLMs into software applications, but it is incremental as it builds on existing testing methodologies.
The authors tackled the problem of bias in Large Language Models (LLMs) by developing LangBiTe, a platform that systematically tests for biases using user-defined ethical requirements, resulting in automated test case generation and execution with end-to-end traceability.
The integration of Large Language Models (LLMs) into various software applications raises concerns about their potential biases. Typically, those models are trained on a vast amount of data scrapped from forums, websites, social media and other internet sources, which may instill harmful and discriminating behavior into the model. To address this issue, we present LangBiTe, a testing platform to systematically assess the presence of biases within an LLM. LangBiTe enables development teams to tailor their test scenarios, and automatically generate and execute the test cases according to a set of user-defined ethical requirements. Each test consists of a prompt fed into the LLM and a corresponding test oracle that scrutinizes the LLM's response for the identification of biases. LangBite provides users with the bias evaluation of LLMs, and end-to-end traceability between the initial ethical requirements and the insights obtained.