What methods or metrics did you use to evaluate how well your LLM performs?

I evaluate LLM performance using several key metrics:

• Perplexity: This measures how surprised the model is by the next word in a sequence. A lower perplexity indicates the model is more confident and accurate in its predictions. For instance, predicting 'apple' after 'I like to eat' is less surprising (lower perplexity) than predicting 'car'.
• Cross-Entropy: This quantifies how well the model's predicted probability distribution aligns with the actual target labels. A lower cross-entropy score signifies a better match between the model's predictions and the ground truth.