Evaluate LLMs with standard metrics
When you evaluate an LLM, you need to measure how well it performs specific tasks. For text generation tasks like translation and summarization, you use metrics that compare generated text to reference examples. For classification tasks, you measure how often the model makes correct predictions. You also need to assess safety and quality through toxicity metrics and human evaluation.
Evaluate text generation
When you need to evaluate text generation tasks like translation or summarization, you compare the generated text to reference examples. Reference text is the ideal or expected output for a given input like a human-written translation or a professionally written summary. Two common metrics for this comparison are BLEU and ROUGE.
BLEU (Bilingual Evaluation Understudy) measures how much of your generated text matches the reference text. It gives you a score between 0 and 1, where higher scores mean better matches.
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of metrics used to evaluate the quality of generated text by comparing it to one or more reference texts. It primarily measures overlap between the generated and reference texts, focusing on recall or how much of the reference content is captured in the generated output.
Measure accuracy for classification tasks
Classification tasks involve choosing one answer from a set of predefined categories like determining if a review is positive or negative, or selecting the correct answer from multiple choices.
Accuracy measures how often a model makes correct predictions. For classification tasks like sentiment analysis or multiple choice questions, accuracy is calculated as the number of correct answers divided by total questions.
Accuracy works well when there's one clearly correct answer, but it's not suitable for open-ended text generation where multiple responses could be valid.
Evaluate text predictability with perplexity
Perplexity measures how predictable your generated text is to the language model. It evaluates how well the model predicts what words should come next in a sentence. Lower perplexity scores indicate the text follows expected language patterns.
You use perplexity to compare models and see which one produces more predictable text patterns.
Assess content safety with toxicity metrics
When you deploy an LLM to serve real users, you need to ensure it doesn't generate harmful, offensive, or biased content. This is crucial because LLMs trained on large internet datasets can learn and reproduce toxic language or biases present in the training data.
Toxicity metrics evaluate whether your model generates this type of content. These metrics help identify potential risks before deployment, allowing you to implement safeguards or additional training to reduce harmful outputs.
Tools like the Perspective API can assess text toxicity, providing scores that indicate the likelihood of content being perceived as harmful or offensive.
Include human evaluations
Human evaluation involves subjective assessments to evaluate quality, relevance, and fluency of generated text. Human evaluators can assess aspects that automated metrics cannot capture, such as creativity, appropriateness, and overall user satisfaction.
Effective human evaluation requires:
- Clear evaluation criteria: Define specific aspects to evaluate, such as coherence, relevance, fluency, and factual accuracy.
- Qualified evaluators: Select evaluators with relevant expertise and use multiple evaluators to increase reliability.
- Systematic analysis: Use evaluation feedback to identify improvement areas and iterate on model performance.
Combining automated metrics with human evaluation provides a more comprehensive assessment of model performance. Automated metrics offer efficiency and consistency, while human evaluation provides nuanced insights into subjective quality aspects.
Interpret evaluation metrics effectively
LLM evaluation metrics require interpretation because they sometimes don't tell the complete story about model performance. Understanding what each metric actually measures helps you make better decisions about your model.
When interpreting LLM metrics, consider the context and limitations of each measurement. A high BLEU score indicates good overlap with reference text, but it doesn't guarantee that the generated text is coherent or appropriate for the situation. Similarly, low perplexity suggests the model is confident in its predictions, but this doesn't mean the content is factually correct or useful.
Multiple metrics together provide a more complete picture than any single score. For example, a model might have excellent fluency scores but poor accuracy on factual questions, or high similarity to reference texts but low creativity ratings. Always evaluate metrics in combination and consider what aspects of performance matter most for your specific use case.
Track evaluation metrics with MLflow
Once you start running evaluations, you'll want to keep track of all your results and experiments. This is where MLflow can help. It's supported in Azure Databricks and helps you organize your evaluation data, experiment results, and model versions.
MLflow lets you log your evaluation metrics, model parameters, and predictions in a standardized way. This means you can compare different model versions, see how performance changes over time, and keep your evaluation results in one place. You can even use the MLflow model registry feature to manage different versions of your models and deploy the best ones directly from Azure Databricks.