The Self-Taught Evaluator addresses this challenge by using a training approach that eliminates the need for human-labeled data. It is built on top of the LLM-as-a-Judge concept, where the model is provided with an input, two possible answers, and an evaluation prompt. The LLM-as-a-Judge model aims to determine which response is better by generating a reasoning chain that reaches the correct result.
Self-Taught Evaluator starts with a seed LLM and a large collection of unlabeled human-written instructions, such as those commonly found in production systems.