Kuidas hinnata kasutaja domeeni täiustatud LLM-i kvaliteeti?

The quality assessment of a fine-tuned LLM in the user domain includes several aspects:

• Accuracy and relevance of responses: checked on a specially prepared test data set from the target domain.
• Quality metrics: such as Perplexity, BLEU, ROUGE, F1-score, as well as specific metrics for tasks (e.g., classification accuracy or completeness).
• Qualitative analysis: manual review of responses by experts to assess appropriateness and usefulness.
• Resilience testing: testing the model on diverse and complex queries to identify weaknesses.
• Comparison with a baseline model: to ensure that the fine-tuning has indeed improved results in the target domain.

Example: if you are fine-tuning an LLM for medical support, create a test set of questions from the medical field and compare the responses before and after fine-tuning, using metrics and expert evaluation.