Mastering the Seven Sins: Effective Evaluation Strategies for Large Language Models
Addressing Common Pitfalls and Ensuring Robust Assessments
Introduction:
OpenAI Evals provide a framework for evaluating large language models (LLMs) or systems built using LLMs. It offers an existing registry of evals to test different dimensions of OpenAI models and the ability to write your own custom evals for use cases you care about. You can also use your data to build private evals which represent the common LLMs patterns in your workflow without exposing any of that data publicly. It can be very difficult and time consuming to understand how different model versions might affect your use case. In the words of OpenAI’s President Greg Brockman:
“evals are often all you need”.
evals are measuring sticks:
Here are some key aspects and features of @OpenAI Evals for LLMs:
1. Benchmarking: OpenAI Evals enables the benchmarking of LLMs against a variety of tasks and datasets. This helps in comparing the performance of different models and tracking improvements over time.
2. Custom Evaluations: Users can create custom evaluation tasks that are tailored to specific use cases or requirements. This flexibility allows for a more targeted…
