Besides automated metrics, what is considered the most reliable method for assessing the quality of text generated by a fine-tuned ChatGPT model?

Besides automated metrics, human evaluation is considered the most reliable method for assessing the quality of text generated by a fine-tuned ChatGPT model. While automated metrics like BLEU and ROUGE scores can provide a quantitative assessment of text similarity and fluency, they often fail to capture subtle aspects of text quality, such as coherence, relevance, and overall understandability. Human evaluators, on the other hand, can assess these subjective qualities and provide a more nuanced and comprehensive evaluation of the generated text. This typically involves having human judges read the generated text and rate it based on various criteria, such as grammatical correctness, factual accuracy, clarity, and usefulness. The human evaluations can then be used to identify areas where the model excels or needs improvement, providing valuable insights for further fine-tuning and optimization. For instance, a human evaluator might recognize that a generated summary, despite having a high ROUGE score, misses a crucial point from the original article. This kind of judgment is difficult for automated metrics to replicate.