LLM as a Judge Evaluation

Evaluating tasks performed by LLMs can be difficult due to their complexity and the diverse criteria involved. Traditional methods like rule-based assessment or similarity metrics (e.g., ROUGE, BLEU) often fall short when applied to the nuanced and varied outputs of LLMs.

For instance, an AI assistant’s answer to a question can be:

• not grounded in context

• repetitive, repetitive, repetitive

• grammatically incorrect

• excessively lengthy and characterized by an overabundance of words

• incoherent

The list of criteria goes on. And even if we had a limited list, each of these would be hard to measure

To overcome this challenge, the concept of "LLM as a Judge" employs an LLM to evaluate another's output, combining human-like assessment with machine efficiency.

Components of LLM as a Judge

The components of an evaluation

There are three components to an LLM evaluation:

1. The input data: Depending on what you are trying to measure or critique, the input data to your evaluation can consist of your applications input, output and prompt variables.

2. The eval prompt template: this is where you specify your criteria, input data, and output labels to judge the quality of the LLM output.

3. The output: the LLM evaluator generates eval labels and explanations to showcase why it gave it a certain label or score.

4. The aggregate metric: when you run thousands of evaluations across a large dataset, you can use your aggregation metrics to summarize the quality of your responses over time across different prompts, retrievals, and LLMs.

LLM evaluation is extremely flexible, because you can specify the rules and criteria in mostly plain language, similar to how you would ask human evaluators to grade your responses.

How it Works

A brief description of how LLMs work as evaluators

Here’s the step-by-step process for using an LLM as a judge:

1. Identify Evaluation Criteria - First, determine what you want to evaluate, be it hallucination, toxicity, accuracy, or another characteristic. See our pre-tested evaluators for examples of what can be assessed.

2. Craft Your Evaluation Prompt - Write a prompt template that will guide the evaluation. This template should clearly define what variables are needed from both the initial prompt and the LLM's response to effectively assess the output.

3. Select an Evaluation LLM - Choose the most suitable LLM from our available options for conducting your specific evaluations.

4. Generate Evaluations and View Results - Execute the evaluations across your data. This process allows for comprehensive testing without the need for manual annotation, enabling you to iterate quickly and refine your LLM's prompts.

Using an LLM as a judge significantly enhances the scalability and efficiency of the evaluation process. By employing this method, you can run thousands of evaluations across curated data without the need for human annotation.

This capability will not only speed up the iteration process for refining your LLM's prompts but will also ensure that you can deploy your models to production with confidence.

Eval Output Types

There are a multiple types of evaluations that we support. Each category of evaluation is categorized by its output type.

Depending on the situation, the evaluation can return different types of results:

Categorical (Binary): The evaluation results in a binary output, such as true/ false or yes/no, which can be easily represented as 1/0. This simplicity makes it straightforward for decision-making processes but lacks the ability to capture nuanced judgements.

Categorical (Multi-class): The evaluation results in one of several predefined categories or classes, which could be text labels or distinct numbers representing different states or types.

Continuous Score: The evaluation results in a numeric value within a set range (e.g. 1-10), offering a scale of measurement. We don’t recommend using this approach.

Categorical Score: A value of either 1 or 0. The categorical score can be pretty useful as you can average your scores but don’t have the disadvantages of continuous range.

Although continuous score evals are an option, we recommend using categorical evaluations in production environments. LLMs often struggle with the subtleties of continuous scales, leading to inconsistent results even with slight prompt modifications or across different models. Repeated tests have shown that scores can fluctuate significantly, which is problematic when evaluating at scale.

Categorical evals, especially multi-class, strike a balance between simplicity and the ability to convey distinct evaluative outcomes, making them more suitable for applications where precise and consistent decision-making is important.

Evals with Explanations

An example of an explanation for an evaluation

It can be hard to understand in many cases why an LLM responds in a specific way. Explanations showcase why the LLM decided on a specific score for your evaluation criteria, and may even improve the accuracy of the evaluation.

You can activate this by adding a flag to the llm_classify and llm_generate functions by specifying provide_explanation=True.

We are always publishing our latest guides on building AI applications. You can read our research, watch our paper readings, join our Slack community, or attend Arize University!