{"id":12957,"date":"2025-02-24T07:21:32","date_gmt":"2025-02-24T15:21:32","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=12957"},"modified":"2025-11-13T19:22:47","modified_gmt":"2025-11-13T19:22:47","slug":"llm-juries-for-evaluation","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/llm-juries-for-evaluation\/","title":{"rendered":"LLM Juries for Evaluation"},"content":{"rendered":"\n
\n
Follow along with the Colab! <\/a><\/div>\n<\/div>\n\n\n\n

Evaluating the correctness of generated responses is an inherently challenging task. LLM-as-a-Judge<\/a> evaluators have gained popularity for their ability to provide nuanced, reference-free, and scalable assessments across diverse tasks. However, individual models still suffer from biases, inconsistencies, and blind spots. <\/span><\/p>\n\n\n\n

\"LLM<\/figure>\n\n\n\n

Ensemble learning, a long-standing technique in traditional machine learning, enhances accuracy and robustness by aggregating multiple models. This principle applies to LLM evaluation<\/a> workflows as well. So-called LLM Juries\u2013 panels of diverse LLM judges<\/a>\u2013 leverage ensembling to improve accuracy, fairness, and interpretability, offering a more robust and reliable LLM evaluation framework<\/a>.<\/span><\/p>\n\n\n\n

In this article, we\u2019ll explore the advantages and limitations of LLM Juries and how to implement one from scratch in Opik<\/a>. <\/span><\/p>\n\n\n\n

What is an LLM Jury?<\/h2>\n\n\n\n

An LLM Jury consists of multiple LLM judges that independently score a given output, then aggregate their scores through a voting function. Unlike a single LLM-as-a-Judge evaluator, which often relies on a large model like GPT-4o, an LLM Jury typically consists of smaller models from distinct families (e.g., GPT, Claude, Command R, Mistral).<\/span><\/p>\n\n\n\n

\"Diagram
An LLM Jury consists of multiple LLM judges that independently score a given output, then aggregate their scores through a voting function.<\/figcaption><\/figure>\n\n\n\n

 <\/p>\n\n\n\n

While larger models handle diverse evaluation tasks well, some smaller or fine-tuned models may struggle with generalization.<\/a> For this reason, each (smaller) model in the jury should support the required scoring type, for example reference-based scoring, or pair-wise scoring.<\/span><\/p>\n\n\n\n

 <\/p>\n\n\n\n

\"Single-point
Each model in the jury should support the relevant scoring type, as smaller models don’t generalize as well to new tasks as larger models do<\/a>.<\/figcaption><\/figure>\n\n\n\n

 <\/p>\n\n\n\n

LLM Juries use traditional ensemble learning techniques for score aggregation, depending on the task. For example, max pooling may be appropriate for binary classification, average or median pooling may be appropriate for rating scales, soft or hard voting for binary or multi-class classification, or stacking for open ended evaluations.<\/span><\/p>\n\n\n\n

Why Use a Jury Instead of a Judge?<\/h2>\n\n\n\n

A single large model like GPT-4o is often used as an evaluator due to its generalizability, depth of knowledge, and reasoning capabilities. However, this approach has significant drawbacks.<\/p>\n\n\n\n

If the same model generates and evaluates responses, self-recognition can lead to self-preference<\/strong>, introducing bias and reducing evaluation fairness.<\/a> This intra-model bias<\/strong> is a serious issue in safety-critical and ethical applications, and can also affect baseline performance.<\/p>\n\n\n\n

Ultra-large models are also expensive<\/strong> and slow<\/strong> due to their high computational requirements. This makes them unsuitable for many real-time, edge, or large-scale applications.<\/p>\n\n\n\n

Research from Cohere<\/a> suggests that a diverse panel of smaller models outperforms a single large judge, reduces bias, and does so at over 7x lower cost. Additionally, multiple smaller models can run in parallel, further improving speed and efficiency.<\/p>\n\n\n\n

\"\"
Research from Cohere suggests that a diverse panel of smaller models outperforms a single large judge, reduces bias, and does so at over 7x lower cost. Image from Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models<\/a><\/figcaption><\/figure>\n\n\n\n

Implementing LLM Juries in Opik<\/h2>\n\n\n\n

Using what we\u2019ve learned so far about LLM Juries, let\u2019s implement one as a custom metric in Opik<\/a>. Follow along in the full-code Colab<\/a> if you aren\u2019t already!<\/p>\n\n\n\n

For this tutorial, we\u2019ll be using a toy subset of the Natural Questions (NQ) dataset<\/a> created by Google Research<\/a> and available in the Hugging Face datasets module<\/a>. It consists of two fields:<\/p>\n\n\n\n