Evals
📰 Medium · LLM
Learn to evaluate LLMs beyond accuracy with three behavioral evals to assess consistency, avoidance, and limitations
Action Steps
- Build a test dataset to evaluate model consistency
- Run avoidance analysis to identify topics or questions the model quietly avoids
- Configure a limitations assessment to determine where the model's knowledge or understanding is lacking
- Test the model's performance using the three behavioral evals
- Compare the results to identify areas for model improvement
Who Needs to Know This
Data scientists and AI engineers can benefit from these evals to improve model reliability and identify potential biases
Key Insight
💡 Behavioral evals can help identify potential biases and limitations in LLMs, improving model reliability and trustworthiness
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🤖 Evaluate LLMs beyond accuracy with 3 behavioral evals! 📊
Key Takeaways
Learn to evaluate LLMs beyond accuracy with three behavioral evals to assess consistency, avoidance, and limitations
Full Article
Three behavioral evals that go beyond accuracy, measuring whether a model answers consistently, what it quietly avoids, and where its… Continue reading on Medium »
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