Reference Based Metric

Reference-based metrics quantitatively assess the quality of generated content (e.g., images, text, code) by comparing it to a human-created reference. Recent research highlights limitations of these metrics, particularly their sensitivity to the choice of reference and their inability to capture nuanced aspects of quality, leading to a growing focus on developing reference-free alternatives. These new approaches leverage techniques like large language models and content-oriented saliency projection to provide objective evaluations without human references, improving efficiency and addressing biases inherent in reference-based methods. The development of robust and reliable evaluation metrics is crucial for advancing various fields, including machine translation, image generation, and automated code review.