Automated Question Generation and Scoring for Climate Change Discussions

The article discusses a new approach to evaluating the quality of answers related to the climate crisis using large language models (LLMs). The authors propose a method for automatically evaluating the knowledge of LLMs by assessing the quality of questions and answers. They suggest five metrics for evaluating questions and five metrics for evaluating answers, which are relevance, clarity, importance, difficulty, innovation, timeliness, readability, depth, answer readability, innovation, and timeliness. The authors use several state-of-the-art LLMs to obtain objective results and take their average. They find that different models have different sensitivity to the timeliness of answers.
The article aims to provide an objective evaluation standard for assessing the quality of answers related to the climate crisis. The proposed method leverages the integration of LLMs to evaluate the quality of questions and answers. By evaluating the quality of both questions and answers, the method can identify the strengths and weaknesses of each and provide a comprehensive understanding of the issues related to the climate crisis.
The authors emphasize the importance of evaluating knowledge related to the climate crisis as it is a complex and multifaceted issue that requires a deep understanding of various factors. Previous studies have relied on perplexity as a measure of assessing generated content, but this method does not accurately assess the knowledge. The proposed approach provides a more comprehensive evaluation by considering multiple aspects of question and answer quality.
The authors also highlight the importance of timeliness in answers, as it is crucial to provide up-to-date information on the climate crisis. They observe that different models have different sensitivity to the timeliness of answers, which suggests that the approach should consider the timeliness of answers when evaluating their quality.
In summary, the article proposes a novel approach for evaluating the quality of answers related to the climate crisis using LLMs. The method considers multiple aspects of question and answer quality and provides an objective evaluation standard for assessing the knowledge of LLMs. The approach is essential for comprehending the complex issues related to the climate crisis and providing effective solutions.