Which metric quantifies the number of errors in text, including insertions and deletions?

The metric that quantifies the number of errors in text, including insertions and deletions, is Word Error Rate (WER). WER is particularly useful in applications such as speech recognition or text generation where assessing the accuracy of the produced text against a reference (or ground truth) is essential. It provides a clear indication of how many edits are needed to convert the generated text into the correct form by encapsulating errors as insertions (extra words), deletions (missing words), and substitutions (incorrect words).

To calculate WER, you take the total number of errors (insertions + deletions + substitutions) and divide that by the total number of words in the reference text. This helps to provide a normalized score that can be used to gauge the performance of generative models in producing coherent and accurate text. The versatility of WER makes it particularly relevant in fields where text fidelity is critical, allowing for a straightforward comparison of various output quality across different models or configurations.

In contrast, metrics such as the F-1 score, recall, and precision focus on different aspects of performance and are often used in classification tasks rather than direct text generation assessments. These metrics do not capture the nuances of how text might differ generationally through insertions or