How to Assess Your LLMs / LLM Applications?

There are a lot of explanations elsewhere, here I’d like to share some example questions and potential answers in an interview setting.

How to Assess the Performance of Our LLMs / LLM Applications?

Here are some tips for readers’ reference:

Benchmark tasks and metrics are well-known for this purpose. Some example metrics are as follows:

Quantitative Metrics:

• Perplexity: Perplexity measures how well a language model predicts a sample of text. Lower perplexity indicates better performance.
• BLEU Score: Commonly used for machine translation, BLEU measures the similarity between model-generated text and human reference text.
• ROUGE Score: ROUGE evaluates text summarization and measures overlap between model-generated and reference summaries.
• F1 Score: For specific tasks like sentiment analysis or named entity recognition, F1 score assesses the model’s precision and recall.
• Accuracy and Precision: For classification tasks, accuracy and precision metrics indicate how well the model classifies input data.

However, those may not apply for your specific LLM application. The general guidance is that: