Part 9/13:
In their experiments, detailed instructions led to responses that retained factual accuracy, included inline citations, and maintained objectivity. Conversely, vague prompts often caused models to hallucinate or stray from factual data, emphasizing the importance of prompt engineering.
Comparing AI Models: Performance and Domain Suitability
Metrics and Results
Using a combination of quantitative (BLEU, ROUGE, METEOR) and qualitative evaluation, the team found:
GPT 3.5 outperformed other models across summarization and QA tasks.
Model size and training data heavily influence performance; GPT 3.5's training on broad web and scientific data gave it an edge.