Describe LLM-as-a-judge for evaluation

  • 7 minutes

LLM-as-a-judge refers to the use of a Large Language Model (LLM) to evaluate the quality and performance of other LLMs and AI systems.

You can use LLM-as-a-judge to use LLMs to evaluate the quality of responses generated by an AI system. By acting as a judge, an LLM can provide consistent and scalable evaluations, which can be useful in scenarios where human evaluation is impractical due to time or resource constraints.

Implement LLM-as-a-judge

LLM-as-a-judge uses prompt engineering and templating. A typical prompt template can be like this:

You will be given a `user_question` and `system_answer` couple. Your task is to provide a 'total rating' scoring how well the `system_answer` answers the user concerns expressed in the `user_question`.

Give your answer as a float on a scale of 0 to 10, where 0 means that the `system_answer` is not helpful at all, and 10 means that the answer completely and helpfully addresses the question. 

Provide your feedback as follows: 

*Feedback*
Total rating: (your rating, as a float between 0 and 10)

Now here are the question and answer to evaluate. 

Question: {question} 
Answer: {answer} 

*Feedback* 
Total rating:

A template like this is designed to assess how well a system-generated answer answers the user's question. Let's explore some of its details:

  • User question and system answer: The template starts by presenting a pair consisting of a user_question and a system_answer. This pair is what the evaluator judges.
  • Task description: The evaluator's task is to provide a 'total rating' that reflects how well the system_answer addresses the concerns in the user_question.
  • Rating scale: The rating is given as a float on a scale from 0 to 10:
  • Feedback format: The evaluator is instructed to provide their feedback in a specific format.
  • Evaluation example: The template then provides placeholders for the actual question and answer to be evaluated:
  • Total score: The evaluator is prompted to provide their feedback again in the specified format.

This structured approach ensures that the evaluation is consistent and focused on how well the system's answer meets the user's needs. It also provides a clear and standardized way to collect feedback. A standardized output format can make it easier to aggregate results across many different samples. The aggregate insights can be useful for improving the language model's performance.

When you use LLM-as-a-judge, it helps to provide few-show examples with human-provided scores for more guidance. Ensure that you're including specific instructions of what good is in relation to your metric, and that you're including a component-based rubric or evaluation scale.

Use MLflow's LLM-as-a-judge capabilities

MLflow enhances the workflow for LLM-as-a-Judge evaluation, making the process more streamlined and efficient.

You can use MLflow's evaluate module to implement LLM-as-a-judge:

  1. Creating example evaluation records: These records serve as the basis for evaluating the model's performance against specific criteria.
  2. Defining a metric object: The metric object serves as a comprehensive framework for assessing the model's performance and includes:
    • The examples.
    • A description of the scoring criteria.
    • The language model used.
    • The aggregations used to evaluate the model across all provided records.
  3. Evaluating the model: Once the metric object is defined, you can evaluate the model against reference datasets using the newly created metric.

By using MLflow, you can create your own custom metrics, and compute these metrics on example datasets. This approach provides a more detailed and accurate evaluation of AI systems, ensuring that they meet the desired standards and perform effectively in real-world scenarios.

Tip

Learn more about creating custom LLM-evaluation metrics with MLflow.

Explore some LLM-as-a-judge metric samples

You can create an LLM-as-a-judge to evaluate any metric by clearly defining how the metric should be assessed and what scoring criteria should be used.

To provide you with some ideas, here are some samples:

Metric Definition
Relevance Evaluate the relevance of the following response to the given query: [Query] - [Response]. Provide a score between 1 and 5, with 5 being highly relevant and 1 being not relevant at all.
Coherence Assess the coherence of the following paragraph. Does it logically flow from one sentence to the next? Provide a score between 1 and 5, with 5 being highly coherent and 1 being not coherent at all.
Accuracy Judge the accuracy of the following statement based on the provided context: [Context] - [Statement]. Provide a score between 1 and 5, with 5 being highly accurate and 1 being not accurate at all.
Fluency Evaluate the fluency of the following text. Does it read naturally and smoothly? Provide a score between 1 and 5, with 5 being highly fluent and 1 being not fluent at all.