Embedding Based Metric

Embedding-based metrics are used to automatically assess the quality of generated text or other structured data like graphs, often correlating these assessments with human judgments. Current research focuses on improving the robustness and reliability of these metrics, particularly addressing their limitations in handling noisy data, diverse domains, and biases inherent in the underlying embedding models, such as BERT. This work is crucial for advancing the development and evaluation of generative models across various applications, including code generation and drug discovery, by providing more accurate and reliable automated evaluation tools. However, challenges remain in ensuring consistent and unbiased evaluations, particularly when dealing with contextualized language models.