Understanding How to Evaluate AI Language Models: A Simple Guide

Introduction

Have you ever chatted with a computer program that seemed to understand you? That’s thanks to AI language models, also known as Large Language Models (LLMs). These are smart programs that can read and write like humans. But how do we know if they’re doing a good job? Let’s break it down in simple terms.

Why Do We Evaluate LLMs?

Evaluating LLMs is crucial for several reasons:

• Safety: To identify potential risks and ensure the model doesn’t produce harmful or inappropriate content.
• Performance: To see how well the model can handle tasks like:
  - summarizing text
  - answering questions
  - translating languages
• Fairness: To check for biases and ensure the model treats all topics and people fairly.
• Improvement: To determine if the model is learning effectively during training.
• Benchmarking: To compare different models and see which one performs best on specific tasks.

“Evaluating LLMs is essential to ensure you’re achieving the best results.”

What Do We Expect from an LLM?

When using an LLM, we expect two main things:

1. Task Completion: The model should effectively perform tasks like summarization, sentiment analysis, question answering, and more.
2. Robustness and Fairness: It should handle unexpected or new inputs well, especially those different from its training data. Additionally, it should be free from biases and treat all topics impartially.

How Do We Evaluate LLMs?

Evaluating LLMs involves several methods, each tailored to assess different aspects of their performance.

1. Automated Metrics and Tools

This is the most commonly used and cost-effective method, requiring no human intervention.

• Accuracy: Measures how often the model’s answers are correct. For example, if you ask, “What’s the capital of Egypt?” it should reply, “Cairo.”

a joke: maybe it should answer “Giza.” if it knows that the king (me) from there

• F1 Score: (Useful in tasks like question answering) It’s separated to:
  - Balances precision (how many selected items are relevant)
  - Recall (how many relevant items are selected).
• ROUGE Score: “Recall-Oriented Understudy for Gisting Evaluation” score is used to check how similar a generated summary is to a reference summary by comparing overlapping words or phrases.

The more overlap, the better the summary.

• BLEU Score: Stands for “Bilingual Evaluation Understudy”
  Commonly used for evaluating machine translation by comparing the model’s output to reference translations.

It looks at both individual words and word order, rewarding closer matches. The higher the BLEU score, the better the translation.

• Levenshtein Similarity Ratio: measures how similar two texts are by counting the fewest single-character changes needed to turn one text into the other.
  It’s better to use it when you want to compare two texts for small differences, like :
  - typos,
  - misspellings,
  - or slight variations in words.
  It’s helpful in tasks like text correction, fuzzy matching, or comparing names and addresses.

A higher Levenshtein Similarity Ratio means the two texts are more similar, so fewer changes are needed. If the ratio is close to 1 (or 100%), it’s a good match.

• Benchmarks: are standardized tests used to evaluate models across various tasks.
  - MMLU (Massive Multitask Language Understanding): Evaluates models on 57 subjects, including math, history, computer science, and law, using multiple-choice questions.
  - While automated benchmarking offers efficiency and standardization, it might overlook nuances that only human evaluators can catch.
• Expected Calibration Error (ECE) and Calibration Metrics:
  - Calibration Metrics (General):
  Definition: Calibration metrics encompass various methods to assess how well a model’s predicted probabilities reflect actual outcomes (e.g., reliability diagrams, Brier score).
  Use: They provide multiple perspectives on calibration performance, helping to diagnose specific issues.
  Good Indicator: Generally, a combination of metrics should be considered for a comprehensive evaluation.
  
  - Expected Calibration Error (ECE):
  Definition: ECE is a specific metric that measures the average difference between predicted probabilities and actual outcomes across all classes.
  Use: It provides a single numerical value that summarizes calibration performance.
  Good Indicator: Lower ECE values indicate better calibration.

ECE is a specific measure within the broader category of calibration metrics, which includes various techniques to assess calibration performance. Both are used to ensure that model predictions are reliable.

2. Using Models as Judges

Sometimes, we use other AI models to evaluate LLMs.

• Powerful General Models: Advanced models like GPT-4 can judge the outputs of other models, providing evaluations that often align with human judgments. However, they can be closed-source and less interpretable.
• Specialist Models: Smaller models trained on specific tasks (like sentiment analysis) can offer consistent and interpretable evaluations, especially if you own them. But they may be less versatile.

A notable approach is GEval, which combines advanced prompting with form-filling techniques. It asks the model to explain its reasoning and then evaluates the output based on predetermined criteria. This method aims to mimic human-like evaluation.

Despite their usefulness, using models as judges has limitations, such as:

• inconsistent scoring
• potential biases.

3. Human Evaluation

Human judgment is invaluable because people can understand nuances that machines might miss.

• Community Feedback (Vibes Check): Community members try out models by giving them specific prompts to see how they respond. They share their impressions, which can be subjective and susceptible to confirmation bias.
• Community Arenas: Platforms where people vote and give feedback on various models, offering a wide range of opinions. Votes are compiled to create dynamic leaderboards.
• Systematic Annotations: Involves paid reviewers following strict guidelines to evaluate outputs. While thorough, this method can be costly.

Having humans in the loop helps capture the qualitative aspects of the model’s performance, such as clarity, coherence, and appropriateness.

Challenges in Evaluation

Evaluating LLMs isn’t always straightforward:

• Subjectivity: Different people might interpret the same response differently.
• Hidden Biases: Models might unintentionally reflect biases present in their training data.
• Dynamic Language: Slang and new expressions can be tricky for models to understand.
• Prompt Sensitivity: Models can produce different outputs based on how the question is phrased.

Combining Methods

The best evaluations often combine automated metrics, model judgments, and human feedback.

• Online and Offline Evaluations: Online evaluations interact with live data and capture real-time user feedback.
  Offline evaluations test models against specific datasets. Both are crucial for a comprehensive assessment.
• Tools like Dynabench: Use human reviewers and AI models to continually improve the testing data, ensuring evaluations stay relevant and challenging.

Why Is Evaluation Essential?

As we continue to develop and deploy LLMs in various applications, ensuring they perform well, are safe, and treat all users fairly is essential. Proper evaluation helps us:

• Improve Models: Identify areas where the model can be enhanced.
• Ensure Safety: Prevent harmful or inappropriate outputs.
• Build Trust: Users are more likely to trust and use models that have been thoroughly evaluated.

“Language is inherently subjective, and while current evaluation systems offer valuable insights, they often fall short of fully reflecting the true capabilities and potential risks of LLMs in this rapidly evolving landscape. Continuous improvement of these evaluation methods is crucial to ensure a more accurate and comprehensive understanding of LLM performance.” Mohamed Reda

Conclusion

AI language models are powerful tools that transform how we interact with technology.
However, they aren’t perfect. Evaluating them thoroughly helps improve their accuracy, fairness, and usefulness.

While challenges exist, combining different evaluation methods gives us the best chance of understanding and enhancing these models.

“It’s remarkable how much we can achieve almost effortlessly and at minimal cost. I’m excited to see just how far this progress will take us.” Mohamed Reda

Thank you for reading, and we look forward to seeing the advancements in this exciting field.