Question: “What are the best methods to evaluate LLM models?”

Answer:

1. Intrinsic Evaluation

This focuses on evaluating the model's performance based on predefined tasks or datasets without human intervention. Common methods include:

Perplexity

• Measures how well a model predicts a sequence of words.
• Lower perplexity indicates better performance.
• Useful for language modeling tasks.

BLEU, ROUGE, and METEOR

• Compare generated text to reference text using n-gram overlap.
• Commonly used in tasks like translation and summarization.

Exact Match (EM) and F1 Score

• EM checks whether the model’s output exactly matches the expected result.
• F1 Score balances precision and recall, especially for tasks like question answering.

Log-Likelihood or Probability

• Assesses the likelihood the model assigns to the correct outputs or gold-standard labels.

2. Extrinsic Evaluation

This assesses the model's performance in real-world tasks and applications. Examples include:

Task-Specific Accuracy