To enhance the evaluation of language model performance on tasks beyond news summarization, several specialized datasets could be constructed. These datasets could focus on various domains and tasks, such as:
Medical Literature Dataset: A dataset that includes research questions extracted from medical papers, along with human evaluations of their quality. This dataset could help in understanding how well language models can extract and evaluate research questions in a highly specialized and technical field.
Legal Document Dataset: A collection of legal documents, including case law and statutes, with annotated research questions and their evaluations. This would allow for the assessment of language models in the context of legal reasoning and the extraction of pertinent questions from complex legal texts.
Technical Manuals and User Guides Dataset: A dataset comprising technical manuals and user guides, focusing on extracting questions related to troubleshooting and usage scenarios. This would help evaluate how well language models can handle instructional and procedural texts.
Social Science Research Dataset: A dataset that includes research questions from social science papers, annotated for quality and relevance. This would provide insights into the performance of language models in extracting questions that address social phenomena and human behavior.
Multilingual Dataset: A dataset that includes research questions from papers written in multiple languages, along with evaluations. This would help assess the performance of language models in cross-linguistic contexts and their ability to handle diverse linguistic structures.
By constructing these specialized datasets, researchers can better evaluate language model performance across various domains, ensuring that evaluation functions are tailored to the specific characteristics and challenges of each field.