USING LLMS FOR EVALUATION

Summary from the TLDR AI newsletter

As large language models become more capable, evaluating them accurately remains challenging due to their diverse tasks. While human evaluation is the gold standard, it's costly and slow. Using LLMs themselves as evaluators offers a scalable, cost-effective alternative, despite potential biases such as positional and verbosity bias, which can be mitigated through techniques like randomizing output positions and using multiple evidence calibrations.