Why Human Evaluation and Annotation Matters in LLM Development

[Why Human Evaluation and Annotation is Important to Build Reliable LLM Systems]

Human evaluation is a critical part of building reliable, controlled, and responsible AI. This ensures that your AI models are aligned with human criteria.

Similarly, human annotation is important to:

• Construct and audit automatic benchmarks
• Create training data and improving LLMs

Depending on the specific problem that needs to be solved, we’ve seen teams using a combination of both automatic evals and human evaluations.

A combination of both automatic and human evals is one of the ways to avoid common pitfalls such as:

• Automation Bias - Over reliance on machine decisions
• Non-alignment of models - models using criteria that do not align with the specific needs of a given project

We’ve already covered a lot of information about automatic evaluations in our previous posts. In this article, we’ll cover Human evaluation and annotation - What are they and why are they important?

Why Human Evaluations

There are two primary goals for using human evaluations:

• To understand an LLM’s behavior and capabilities around response generation:
  • Is the model biased/faithful?
  • What are the critical vulnerabilities and shortcomings of the model?
  • Is the model capable of generating summaries, writing code, correct token prediction etc.
• To compare 2 (or more) different LLMs:
  • Which model is better for a specific use case
  • Is the model better than existing benchmarks?

Why should you think about a Hybrid Evaluation System?

Both Automatic and human eval systems have their pros and cons. Using them together helps in building more robust and reliable solutions:

Human Evaluation vs Human Annotation

Human annotation and human evaluation are two critical components in the assessment and development of LLMs. While they are often used interchangeably, they serve different purposes and methodologies.

Human Annotation

Human annotation involves the process of labeling data, categorizing text and providing specific labels to the datasets.

• Data Preparation: Human annotations are essential for constructing high-quality training datasets that improve model performance. They ensure that the data used for training is accurate and relevant.
• Benchmark Creation: Annotations help in creating benchmarks for automatic evaluation metrics, serving as a reference point against which model outputs can be compared.

Human Evaluation

Human evaluation assesses the performance of LLMs based on reviewing their outputs to determine its quality, relevance, coherence, and other qualitative aspects.

• Performance Assessment: Human evaluation aims to determine how well a model performs in generating text that meets user expectations or specific criteria.
• Utility Measurement: It evaluates whether the outputs created by a model provide value when integrated into larger applications or systems.

Human evaluation involves asking individuals to review a given piece of information and provide feedback based on their understanding or response to specific questions.

Types:

• Binary - Yes/No questions
• Likert scale - Rating the response on a scale of 1 to 5
• Categorical scale - Bucketing the responses in different categories or tiers
• Open-ended feedback

Challenges associated with Human Evaluations

Here are a few challenges associates with human evalautions:

• Order Bias
• The order in which the questions are asked or information is shown to annotators can influence the outcomes
• Scale Calibration
• Responses for Likert and Categorical scales could vary depending on subjective perception of individuals
• Low agreement scores
• For subjective type questions, the agreement score between annotators might be low

Conclusion

Utilizing both automatic and human evaluations creates a robust framework for evaluating LLMs.

This hybrid approach utilizes the strengths of each method - efficiency and scalability from automatic evaluations and the nuanced understanding provided by human assessments.

This leads to more reliable and comprehensive evaluations of model performance across diverse tasks and applications.