60Stand up an internal Chatbot Arena to decide if model B beats A. How many comparisons, and which biases bite?▼hardOpenAIAnthropicLMSYS1 replies◆ premiumAveraging two scalar scores tells you almost nothing. Blind pairwise battles plus a win-rate confidence interval do, and the result is only trustworthy once you've controlled position bias, verbosity bias, and rater fatigue.Open full answer →
61How do you evaluate an LLM's output quality?▼mediumOpenAIAnthropicScale AI2 replies◆ premiumThere's no single number. The trap is reaching for perplexity or BLEU because they're easy to compute, then optimizing a score that has nothing to do with whether the output is good. The move is to match the metric to the task and name where each one lies to you.Open full answer →