How to Automatically Catch Mistakes from Large Language Models
Evaluating LLMs (like GPT-4) can be tricky. They generate convincing text, but not always accurate or appropriate. So how do you automatically spot those errors?
Here are four methods you can use to catch common LLM issues like hallucinations, offensive content, or off-topic answers:
1. Reference Comparison (Gold-Standard Check)
How it works: Compare the model’s output to a high-quality, human-written reference answer. Good for: Fact-based tasks with a clear “correct” answer. Tools: ROUGE, BLEU, or embedding similarity (e.g., cosine score). Limitations: Doesn’t work well for open-ended or creative tasks.
2. LLM-as-a-Judge (Self-Check with Prompts)
How it works: Use another LLM to review the output and rate it for accuracy, relevance, or tone. Good for: More nuanced evaluations where you don’t have a gold standard. Tools: Prompted reviews like “Rate the helpfulness of this answer on a scale from 1–5.” Limitations: Can be biased or inconsistent, depending on how the LLM is prompted.
3. Rule-Based Checks (Hardcoded Red Flags)
How it works: Use regex or keyword-based rules to catch forbidden phrases, profanity, or broken formatting. Good for: Safety, compliance, and formatting checks. Tools: Custom Python scripts, regex matchers. Limitations: Rigid—can’t catch subtle errors or generalize well.
4. Model-Based Classifiers (Trained Review Models)
How it works: Train a classifier to predict if an output contains a specific type of error (like toxicity or factual mistakes). Good for: Scaling evaluations across lots of data with less human effort. Tools: Fine-tuned models like BERT or RoBERTa. Limitations: Needs a labeled dataset to start with.
| Method | Best For |
| Reference Comparison | Clear correct answers |
| LLM-as-a-Judge | Open-ended or subjective tasks |
| Rule-based checks | Simple, black-and-white violations |
| Classifiers | High-volume, custom error detection |
Summary: When to Use EachTip: Combine Methods for Best Results
Most teams use a mix, for example, rule-based checks for safety, LLMs to rate tone, and gold references for accuracy. Tools like Label Studio make it easy to integrate all four.
Want to Go Deeper on AI Evaluations?
If you're looking to expand beyond LLMs and understand how evaluations work across the AI lifecycle, classification, generation, multi-modal models, and more, check out our full guide:
It covers real-world use cases, evaluation frameworks, and how to combine human and automated methods to build trustworthy systems.
Frequently Asked Questions
Frequently Asked Questions
1. Why is evaluating LLM outputs so difficult?
LLMs generate open-ended, nuanced language. Unlike tasks with a clear “right” answer, many LLM responses are subjective, making it hard to judge quality automatically. They can also “hallucinate” or introduce errors that sound plausible, making detection more challenging.
2. What’s the best way to evaluate LLM outputs?
There’s no single best method. For tasks with known answers, reference-based comparison works well. For subjective or creative tasks, using an LLM-as-a-judge or trained classifier is more effective. Most teams combine multiple methods to cover different types of errors.
3. Can LLMs evaluate themselves accurately?
Sometimes. LLM-as-a-judge methods can be surprisingly effective, especially for subjective traits like tone, clarity, or helpfulness. But they’re still prone to bias and inconsistency, so human review or additional checks are recommended.
4. What’s the difference between rule-based checks and classifier-based checks?
Rule-based checks rely on manually written rules (like keyword filters), while classifier-based checks use machine learning to detect patterns in data. Rule-based checks are fast but rigid; classifiers are more flexible but require labeled training data.
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