The challenges of LLM evaluation

LLMs are often used as evaluators themselves, playing a crucial role in aligning other models with human preferences or improving their own performance during training. This is especially important for tasks where multiple valid answers are possible, as is often the case with creative or complex instructions.

However, training accurate LLM evaluators typically relies on extensive human-annotated data, which is costly and time-consuming to acquire. This bottleneck becomes self-defeating, hindering the rapid development and deployment of new LLM-based applications.