Question answering systems are designed to handle a wide range of questions, including those with multiple sub-questions. Here's how they typically handle such questions:

  1. Understanding the question structure: Question answering systems use natural language processing techniques to analyze the grammatical structure and semantics of the question. They can identify sub-questions by detecting different clauses, phrases, or interrogative words within the overall question.

  2. Breaking down the sub-questions: Once the system has identified the sub-questions, it can break them down into separate queries. Each sub-question can be processed individually to generate relevant answers.

  3. Contextual understanding: To answer sub-questions effectively, question answering systems take into account the context provided in the overall question. This context is crucial for interpreting and generating accurate answers. The system may use techniques like coreference resolution to link pronouns or noun phrases in the sub-questions to entities mentioned earlier.

  4. Knowledge retrieval and analysis: The system retrieves relevant information from structured or unstructured data sources such as databases, text corpora, or the web. This information is analyzed to match it with the sub-questions and extract the relevant facts or answers required.

  5. Answer synthesis: Once the system has retrieved relevant information for each sub-question, it synthesizes the answers into a coherent response. This synthesis may involve aggregating information from multiple sources, selecting the most relevant facts, or generating a concise and informative answer.

  6. Presentation of answers: Finally, the question answering system presents the answers to the multiple sub-questions in a suitable format. This could be a simple textual response or a more complex form, such as a bullet list or a structured table, depending on the nature of the question and the expected output.

  It's worth noting that the effectiveness of question answering systems in handling questions with multiple sub-questions largely depends on the complexity and variety of the question types they have been trained or programmed to handle. Some systems may excel in certain domains or question types while struggling with others. Overall, improvements in natural language processing, machine learning, and knowledge representation techniques continue to enhance the capabilities of question answering systems in handling complex questions.