Spade: An Innovative Approach to Synthesize Assertions for Identifying Errors in Large Language Models

A team of researchers from UC Berkeley, HKUST, LangChain, and Columbia University have developed a new system called Spade that automatically generates tests to identify errors in large language models(LLMs) like ChatGPT, Gemini, Claude, and Others.

Published on January 5th, 2024,

The research demonstrates how Spade can generate customized tests to evaluate AI-powered data generation pipelines without the need for extensive training data.

LLMs such as ChatGPT have gained immense popularity in recent years due to their capability to generate human-like text and engage in natural conversations. However, these models are susceptible to unpredictable failures, such as generating inappropriate, incorrect, or nonsensical responses. As more companies incorporate LLMs into their data generation pipelines, it is crucial to have rigorous tests to identify these failures before integrating them into production systems.

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