This is an example of the open source LLM evaluation platform multinear: based on the standard answers

This is an example of the open source LLM evaluation platform multinear: based on the standard answers related to a type of input question, dozens of similar types are generated to detect LLM applications and evaluate the same type of answers to cover all relevant scenario cases.

LLM evaluation quality determines the actual value of the LLM application and the experience that users can directly feel.

Figure 2 is a normal LLM application.

Based on the standard answers related to a type of input questions, dozens of similar types are generated to detect the LLM application and evaluate the answers of the same type to cover More relevant scenario cases.

Figure 2 is a normal LLM application development process.

If you want your AI product to achieve a good effect, you must repeatedly test, evaluate, and optimize, which takes a lot of time.

The role of the LLM evaluation framework is to help you generate the data needed for evaluation and conduct evaluations in batches, thereby improving the quality of AI products and increasing efficiency.

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