Methods to Evaluate Foundation Model Performance

While choosing the right FM for our applications is important, assessing its effectiveness by analyzing its strengths and weaknesses is equally crucial. Large language models are prone to generating hallucinated, toxic, or biased responses, which adds to the evaluation stage’s importance.

Evaluating foundation models ensures they are aligned with desired outcomes regarding accuracy, relevance, and efficiency. For example, a model used in customer support needs to provide accurate responses promptly, while a language model may require a review of fluency and coherence. AWS Bedrock provides tools and services to simplify and enhance evaluation processes, supporting regular monitoring to keep models aligned with organizational needs and user expectations.

Methods to evaluate foundational models

Following are some of the methods we can use to evaluate foundational models:

Human evaluation

In human evaluation, we bring in human judgment to analyze a foundation model in the form of human workers as a team that could be employees from the company we work in or a separate team of experts from the industry. Evaluators rate outputs based on criteria like helpfulness. Amazon Bedrock allows us to integrate human feedback while fine-tuning our models, which is useful for complex or nuanced tasks.

For example, let’s assume a legal firm is deploying an AI model to summarize lengthy contracts. The firm assembles a group of legal experts to review AI-generated summaries and rate them based on completeness and legal accuracy. If certain clauses are frequently omitted, the firm uses this feedback to fine-tune the model for better performance.

Benchmark datasets

Benchmark datasets, like GLUE and SQuAD, provide standardized tests for tasks such as sentiment analysis or summarization. They enable comparison across models and help assess model performance relative to industry standards.

To see benchmark datasets in action, let’s assume an organization is testing a foundation model for sentiment analysis of customer reviews. They use the IMDB Sentiment Dataset, a well-known benchmark, to evaluate how accurately the model classifies reviews as positive or negative. If the model scores below the industry benchmark, the company considers further training on financial-specific datasets.

A/B testing

In this method, we deploy two model versions to compare performance on separate data subsets. This can reveal which model provides better customer support responses. This approach offers real-world comparisons, which are valuable for fine-tuning business applications.

For example, let’s assume a company developing a customer support chatbot wants to improve response accuracy and user satisfaction. They test:

• Model A: A general-purpose foundation model optimized for speed.

• Model B: A fine-tuned version trained on past customer interactions.

For one month, half of the customers interact with Model A, while the other half interact with Model B. The company collects feedback on response helpfulness, measures response latency, and tracks escalation rates to human agents. If Model B shows higher user satisfaction and lower escalations, the company can confidently deploy it as the final model.

Metrics to evaluate foundational models

The following metrics are commonly used to assess the accuracy, quality, and consistency of foundational models:

• ROUGE (Recall-Oriented Understudy for Gisting Evaluation): ROUGE measures the similarity between generated and reference text by comparing overlapping n-gramsn n-gram is a sequence of n consecutive words or characters in a text. It’s often used for evaluating summarization tasks, where the goal is to capture essential content in a shorter form.

• BLEU (Bilingual Evaluation Understudy): BLEU measures the accuracy of generated text in relation to reference text by comparing word overlap. It’s particularly useful in machine translation, where close adherence to reference translations is critical.

• BERTScore: BERTScore uses embeddings from BERT (Bidirectional Encoder Representations from Transformers) to analyze the semantic similarity between model-generated and reference text. This metric captures both word overlap and contextual meaning, making it useful for assessing complex text tasks.

• Latency: Latency measures response time, which is crucial for real-time applications like chatbots or live customer support systems.

• Cost efficiency: Cost efficiency tracks computational resource usage, helping to determine if the model’s performance is justified within budget constraints. AWS Bedrock provides tools to monitor and optimize resource usage for cost-effective model performance.

By using a combination of these metrics, we can measure model effectiveness across different dimensions, ensuring comprehensive insights into performance.