How to Build a Custom LLM Benchmark for Accurate Evaluation

In our last blog, we discussed why standard, publicly available large language model (LLM) benchmarks are becoming increasingly unreliable. A recent study found that popular evaluation sets often fail to meaningfully reflect how models perform in real-world applications. In fact, models that scored similarly on standard benchmarks diverged significantly when tested on more realistic tasks.

With widespread availability online, these datasets often become part of a model’s training data, making it hard to evaluate how well an LLM can generalize or perform in real-world scenarios. For example, in translation tasks, FLORES is a widely used public benchmark, but many models include it in their training data, often without disclosing it. As a result, strong performance on FLORES may reflect memorization rather than true translation ability, making the benchmark unreliable for evaluation.

That’s why more teams are turning to custom benchmarks, where test sets are designed specifically for their model, use cases, and domains. However, building a custom benchmark isn’t as simple as writing a few prompts and checking the outputs. To generate meaningful evaluation data, your benchmark must be intentional and include a human-in-the-loop component.

Key Considerations for Building Custom LLM Benchmarks

Designing a benchmark from scratch requires more than domain expertise. It’s a multi-step process that benefits from thoughtful planning and a combination of automation and human insights.

1. Define Your Objective

Start with clarity: What exactly are you trying to measure? Factual accuracy? Fluency? Multilingual performance? Safety and bias? Even seemingly simple classification tasks can suffer from mismatched label definitions when using a public benchmark—what one dataset calls “neutral” sentiment may mean no emotion, while another interprets it as mixed sentiment. Align your benchmark with the capabilities that matter most for your product or users and define and align your labels to match your internal criteria.

2. Choose the Right Task Types

Not all benchmarks look the same. You might need:

• Classification (intent recognition, sentiment analysis)
• Open-ended generation (summarization, email drafting)
• Question answering (short-form or long-form)
• Translation or multilingual performance
• Ranking or retrieval-based tasks

The same study points out that standard benchmarks disproportionately favor models trained for multiple-choice or short-answer formats. However, real-world tasks require different data structures, evaluation methods, and human involvement, so it’s important to choose tasks and metrics that align with the model’s intended function.

3. Design Representative Prompts and Inputs

A strong benchmark accounts for linguistic, cultural, and regional variation, especially for products meant to scale globally. Make sure your test set covers the full range of complexity and diversity your model will face in production. For instance:

• Vary sentence length, tone, and ambiguity
• Include edge cases and rare examples
• Represent multiple dialects, personas, slang, abbreviations, etc.

This step is especially important for multilingual and culturally specific use cases.

4. Incorporate Domain Expertise

Certain use cases demand specialized knowledge for both data creation and evaluation.

• STEM benchmarks: They often require precise logic, reasoning, and mathematical fluency beyond standard language tasks.
• Medical benchmarks: These need subject-matter experts to judge the accuracy and safety of model-generated outputs, especially when dealing with patient care or diagnosis.
• Finance-related tasks: They require familiarity with terminology, regulation, and the ability to distinguish between compliant and non-compliant responses.

Many public benchmarks rely on distant supervision or synthetic annotations to scale quickly. While useful for training, these methods often result in noise or mislabeled data and are unsuitable for reliable evaluation. These domains can’t be evaluated effectively without incorporating human expertise into both dataset design and evaluation.

Why Human-in-the-Loop is Essential for Benchmarking

1. Reliable ground truth creation

Human annotators or domain experts create reference outputs or label correct responses, which serve as the gold standard. Learn how our global team of annotators helped IBM classify prompts into multiple categories here.

2. Subjective evaluation at scale

While automated metrics like BLEU or ROUGE can provide quick evaluations, they often miss the nuance required for meaningful assessment. In addition, they collapse quality into a single number, when there are multiple factors independently contributing to the quality of a response. Humans can rate outputs based on clarity, helpfulness, factuality, tone, cultural appropriateness, or bias.

3. Flexible and domain-specific QA

HITL allows you to tailor evaluation criteria to your industry, whether legal, healthcare, finance, or entertainment. Subject-matter experts are necessary to ensure evaluations are accurate and compliant.

4. Multilingual and multicultural insight

Native speakers can catch issues that automated systems can’t, such as idiomatic errors, tone mismatches, or inappropriate content. Annotation errors are also common in public benchmarks, especially those created via crowdsourcing. Without rigorous quality control, careless automated annotation can introduce errors and unfair advantages to models similar to those used in the labeling process.

Upgrade Your Benchmark for Smarter LLM Evaluation

Custom LLM benchmarks aren’t just a workaround for public dataset contamination. They’re a smarter, more precise way to evaluate real-world model performance. But to be effective, these benchmarks must be designed with intent, complexity, and human insight in mind.

Ready to design a benchmark that reflects your real-world use case? Contact us today or explore our Generative AI Training and Data Collection services to learn more.