What is BERTScore or other embedding-based metrics, and can they be helpful in evaluating the similarity between a generated answer and a reference answer or source text?

BERTScore is an embedding-based metric used to evaluate the similarity between texts by leveraging contextual embeddings from models like BERT. Unlike traditional metrics such as BLEU or ROUGE, which rely on exact word matches or n-gram overlaps, BERTScore compares texts by measuring the semantic similarity of their token or sentence embeddings. Other embedding-based metrics include MoverScore, which uses optimal transport to align embeddings, and Sentence-BERT, which computes sentence-level similarities. These approaches capture deeper semantic relationships, making them useful for tasks where paraphrasing or varied phrasing is common.

Embedding-based metrics work by converting text into high-dimensional vectors using pre-trained language models. For example, BERTScore computes token-level embeddings for both the generated and reference texts, then calculates precision, recall, and F1 scores based on cosine similarity between these embeddings. If a generated answer uses synonyms or rephrases concepts (e.g., “canine” instead of “dog”), BERTScore can recognize the semantic equivalence even if surface-level words differ. Similarly, Sentence-BERT generates a single embedding per sentence, enabling efficient comparison of entire sentences or paragraphs. These methods are particularly effective when evaluating tasks like summarization or question answering, where meaning matters more than exact wording.

Embedding-based metrics are helpful for evaluating answer similarity but have trade-offs. Strengths include better alignment with human judgment on semantic tasks and robustness to paraphrasing. For instance, a generated answer like “The process converts sunlight to energy” would score higher against a reference stating “Photosynthesis transforms solar energy” compared to BLEU, which might miss the connection. However, these metrics require computational resources to generate embeddings and may not always correlate perfectly with human evaluation. Additionally, they depend on the quality of the underlying model—BERTScore’s effectiveness, for example, is tied to BERT’s training data and architecture. Developers should consider combining embedding-based metrics with traditional methods and human evaluation for a balanced assessment, especially in critical applications like fact-checking or legal document analysis.