Using LLM-as-a-Judge: Helpful or Harmful?

Today’s presentation is titled "Using LLMs as a Judge: Helpful or Harmful?" It’s a timely topic, and we’re excited to have Michaela Kaplan, our ML Evangelist, here to present and answer your questions.

Hi everyone, I’m Michaela, the ML Evangelist at HumanSignal. That’s a fancy way of saying I used to be a practicing data scientist, and now I talk a lot about data science. My background is mostly in NLP, so the question of using LLMs as evaluators—“LLMs as a judge”—is both relevant and important. Feel free to drop questions in the chat or Q&A section as we go.

Let’s say you’ve got some data—shapes, colors, or anything else—that you need to organize or evaluate. The big question is: is your data high-quality? Are your annotators labeling things correctly? Is your model doing what you think it’s doing—and how can you be sure?

Traditionally, we rely on human annotation. This example shows sentiment classification on movie reviews (positive, negative, neutral). With human labels, we can calculate precision, recall, and F1—real metrics that tell us how well our model is performing. But human annotation is expensive and slow.

That’s where the idea of using LLMs as judges comes in. Instead of a person reviewing the model’s output, you ask an LLM: is this response correct? Is it relevant? That sounds efficient—but it can lead to problems. Sometimes you end up with one LLM evaluating another LLM, and it’s unclear who’s doing what or how trustworthy the results are.

There are pros: LLMs are faster and cheaper. A model can review a sample in seconds. They're scalable—you can parallelize evaluations. They can also explain their reasoning, which is helpful for debugging.

But there are cons too. LLMs are prone to bias. There’s position bias (favoring the first option), verbosity bias (favoring the wordiest response), and self-enhancement bias (favoring answers that resemble their own style). Each of these can skew results. And the LLM may lack context, especially for proprietary data. If your dataset wasn’t in its training set, it might not judge accurately.

So how do we trust an LLM as a judge? One solution is to create a small test set labeled by humans. This gives you a gold standard to compare against. You can also use it for prompt tuning and calibration. That’s supported in Label Studio via our Prompts feature (available in Starter Cloud and Enterprise). A hands-on approach helps you understand where the model does well—and where it needs work.

Next, evaluate multiple dimensions of correctness. Accuracy alone isn’t enough. You need to check for relevance, clarity, and faithfulness—especially in generative tasks. In Label Studio, our RAG evaluation template shows how to combine human input with metrics like RAGAS for faithfulness and relevance.

A third approach is to use an ensemble of models—“LLM as a jury.” This idea comes from the 2024 paper “Replacing Judges with Juries.” It shows that averaging scores across different LLMs (e.g., Claude, Mistral, GPT) can get you closer to human judgment than relying on one model alone.

Ultimately, there’s a trade-off. A fully manual evaluation is most accurate but expensive. A fully automated LLM-as-judge workflow is efficient but less reliable. The best approach is hybrid: use LLMs to scale up, but always validate against human-reviewed ground truth.

Label Studio lets you implement this. In open source, you can use ML backends. In Starter Cloud or Enterprise, you can use Prompts. It supports prompt versioning, model switching, and automatic accuracy comparisons. You can evaluate multiple prompts, compare outputs to human annotations, and iterate quickly. This helps you fine-tune both your models and your evaluation workflows.

We also covered prompt tuning, rubric design, and how to align LLMs with human scoring—especially for summarization or classification. Label Studio enables all of this with structured interfaces and flexible evaluation pipelines.

To wrap up: LLMs as judges aren’t a silver bullet. But when used carefully—with prompts, tuning, and oversight—they can accelerate evaluation and reduce costs. The key is maintaining human oversight and constantly checking for model drift and bias. For high-stakes use cases (like healthcare or finance), you need to be extra cautious.

If you have more questions, feel free to email me at mikaela@humansignal.com. And thank you all for your time!