Evaluate LLMs with standard metrics
Evaluating Large Language Models (LLMs) is an important step in understanding their effectiveness and ensuring they meet the desired outcomes for specific applications.
You can use various evaluation metrics to evaluate the performance of the LLM you're using. You can use standard metrics to evaluate aspects like the model's ability to produce accurate and coherent responses, or evaluate metrics that provide insights into the ethical considerations related to model deployment.
Three of the most commonly used evaluation metrics are BLEU, ROUGE, accuracy, perplexity, and toxicity. Next to standard metrics that you can use to automatically evaluate your LLMs, you also want to include human evaluations.
Let's explore each of these metrics in more detail.
Use BLEU and ROUGE to evaluate the quality of generated texts
BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) are often used to assess the quality of texts generated by LLMs. BLEU and ROUGE are commonly used in tasks like machine translation and summarization, where the model's output is compared to a reference text.
BLEU measures the precision of n-grams (sequences of n words) in the generated text that match the reference text. BLEU scores range from 0 to 1, with higher scores indicating better performance. BLEU focuses on precision, which means it measures how much of the generated text is present in the reference text.
ROUGE measures the overlap of n-grams between the generated text and the reference text. ROUGE has several variants, including ROUGE-N (measuring n-gram overlap), ROUGE-L (measuring the longest common subsequence), and ROUGE-S (measuring skip-bigram overlap). ROUGE focuses on recall, which means it measures how much of the reference text is present in the generated text.
Understand a model's accuracy
Accuracy is a fundamental evaluation metric used across various machine learning models, including LLMs.
When you assess the accuracy of an LLM, you measure how often the model's output aligns with the expected or correct result.
For example, in tasks such as text classification, you can measure accuracy by the proportion of correct predictions out of the total predictions made by the model.
High accuracy indicates that the model is making correct predictions consistently, which is crucial for tasks that require precise and reliable outputs.
However, accuracy alone isn't sufficient for evaluating generative models like LLMs, as these models often generate text with multiple plausible outputs.
Understand a model's perplexity
Perplexity measures how well a probability model predicts a sample of data.
Specifically, it quantifies the uncertainty in predicting the next word in a sequence.
Lower perplexity values indicate that the model is more confident and accurate in its predictions, leading to more coherent, and contextually appropriate outputs.
Perplexity is especially useful when comparing different models or versions of a model to determine which one performs better in generating natural language text.
Understand a model's toxicity
Toxicity is a metric that evaluates the ethical and social implications of deploying LLMs in real-world applications. Toxicity refers to the potential for a model to generate harmful, offensive, or biased content.
As LLMs are trained on large datasets sourced from the internet, they can inadvertently learn and reproduce toxic language or biases present in the data.
Evaluating toxicity helps you to ensure that the model doesn't perpetuate harmful stereotypes, contribute to online harassment, or produce content that could be damaging to users.
You can use tools like the Perspective API to assess the toxicity of text, allowing you to identify and mitigate these risks before deploying the model.
Include human evaluations
Human evaluation involves subjective assessments by individuals to evaluate the quality, relevance, and fluency of the generated text. Implementing manual evaluations can provide new insights and help you refine models based on nuanced human preferences and expectations.
When you want to assess the performance of an LLM manually, keep the following aspects in mind:
- Define clear evaluation criteria: Establish well-defined rubrics that outline the specific aspects of the generated text to be evaluated. Common criteria include coherence, relevance, fluency, and factual accuracy.
- Select qualified evaluators: Choose evaluators who have expertise relevant to the task, and include multiple evaluations to increase reliability.
- Analyze and iterate: Use the feedback from human evaluations to identify areas where the model can be improved. Iterate on the model and continue to refine it based on human preferences and expectations.
A combination of automated metrics and human evaluations helps you to establish a more robust and comprehensive assessment of your model's performances. Automated standard metrics can provide you with quick insights, while human evaluations, though expensive and time-consuming, can give you a more nuanced perspective.
Track evaluation metrics with MLflow
You can use MLflow for tracking, comparing, and managing model performance metrics across different experiments in Azure Databricks.
MLflow allows you to log key parameters, predictions, and evaluation metrics of LLMs in a standardized format. By using MLflow's model registry and experiment tracking capabilities, you can easily compare different model versions, monitor their performance over time, and deploy the best-performing models directly from Azure Databricks.