How to evaluate model hallucinations?

One-sentence conclusion

Establish a fixed evaluation set and separately count correct, incorrect, refused, and unverifiable responses.

Use Cases

For enterprise AI teams that demand high accuracy.

Key Points

1. 1

   Build a gold-standard dataset covering high-frequency and edge cases

2. 2

   Please provide verifiable citations for the answer.

3. 3

   Allow the model to refuse answering when evidence is insufficient

Common Misconceptions

• Just a few demo questions to gauge performance; no fixed evaluation set.

Comparison Criteria