Metrics like BLEU and ROUGE compare word overlap, not meaning. An LLM's output like "the cat is on the bed" could be semantically opposite to the ground truth "the cat is on the mat" but still score high. This highlights the need for more sophisticated, meaning-aware evaluations like LLM-as-a-judge.
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Simply creating an LLM judge prompt isn't enough. Before deploying it, you must test its alignment with human judgment. Run the judge on your manually labeled data and analyze the results in a confusion matrix. This helps you see where it disagrees with you (false positives/negatives) so you can refine the prompt and build trust.
Popular benchmarks like MMLU are inadequate for evaluating sovereign AI models. They primarily test multiple-choice knowledge extraction but miss a model's ability to generate culturally nuanced, fluent, and appropriate long-form text. This necessitates creating new, culturally specific evaluation tools.
Standard automated metrics like perplexity and loss measure a model's statistical confidence, not its ability to follow instructions. To properly evaluate a fine-tuned model, establish a curated "golden set" of evaluation samples to manually or programmatically check if the model is actually performing the desired task correctly.
As benchmarks become standard, AI labs optimize models to excel at them, leading to score inflation without necessarily improving generalized intelligence. The solution isn't a single perfect test, but continuously creating new evals that measure capabilities relevant to real-world user needs.
When using an LLM to evaluate another AI's output, instruct it to return a binary score (e.g., True/False, Pass/Fail) instead of a numbered scale. Binary outputs are easier to align with human preferences and map directly to the binary decisions (e.g., ship or fix) that product teams ultimately make.
Seemingly simple benchmarks yield wildly different results if not run under identical conditions. Third-party evaluators must run tests themselves because labs often use optimized prompts to inflate scores. Even then, challenges like parsing inconsistent answer formats make truly fair comparison a significant technical hurdle.
Do not blindly trust an LLM's evaluation scores. The biggest mistake is showing stakeholders metrics that don't match their perception of product quality. To build trust, first hand-label a sample of data with binary outcomes (good/bad), then compare the LLM judge's scores against these human labels to ensure agreement before deploying the eval.
When creating an "LLM as a judge" to automate evaluations, resist the urge to use a 1-5 rating scale. This creates ambiguity (what does a 3.2 vs 3.7 mean?). Instead, force the judge to make a binary "pass" or "fail" decision. It's a more painful but ultimately more tractable and actionable way to measure quality.
A one-size-fits-all evaluation method is inefficient. Use simple code for deterministic checks like word count. Leverage an LLM-as-a-judge for subjective qualities like tone. Reserve costly human evaluation for ambiguous cases flagged by the LLM or for validating new features.
Using an LLM to grade another's output is more reliable when the evaluation process is fundamentally different from the task itself. For agentic tasks, the performer uses tools like code interpreters, while the grader analyzes static outputs against criteria, reducing self-preference bias.