Measuring the Quality of Answers in Political Q&As with Large Language Models

Understand

This article proposes a new approach for assessing the quality of answers in political question-and-answer sessions.

• We measure the quality of an answer based on how easily and accurately it can be recognized in a random set of candidate answers given the question's text.
• This measure reflects the answer's relevance and depth of engagement with the question.
• Like semantic search, we can implement this approach by training a language model on the corpus of observed questions and answers without additional human-labeled data.