Large language models (LLMs) excel at various tasks, but how well do they handle *complex, multi-part questions*? A new research paper introduces "Compound-QA," a benchmark designed to test LLMs' ability to tackle compound questions—those with multiple sub-questions within a single query. Think of it like asking, "What's the weather forecast for the next three days, and should I pack an umbrella?" Humans can seamlessly address such questions, but LLMs struggle. This research dives into why that is and explores potential solutions. The Compound-QA benchmark categorizes compound questions into five types: Factual-Statement, Cause-and-Effect, Hypothetical-Analysis, Comparison-and-Selection, and Evaluation-and-Suggestion. Each type tests different aspects of understanding, reasoning, and knowledge. Researchers tested eight open-source LLMs and discovered a significant performance drop compared to single-question tasks. Interestingly, models performed best on Factual-Statement questions, likely due to their weaker interdependencies between sub-questions. The most challenging type? Evaluation-and-Suggestion, demanding advanced comprehension, reasoning, *and* persuasive generation. Further investigation revealed that LLMs do much better with individual questions in a multi-turn dialogue than when presented with those same sub-questions bundled together as a compound query. The position of sub-questions also matters. Just like humans can sometimes lose focus during lengthy questions, LLMs demonstrated the best performance on the first sub-question, with accuracy declining for subsequent ones. To improve LLM performance, the researchers experimented with techniques like chain-of-thought prompting, few-shot learning, and fine-tuning. Encouragingly, each method led to improvements. A key takeaway from this research is that while LLMs are impressive, they still need to develop their capacity for complex, human-like reasoning. Compound-QA provides a valuable benchmark for measuring progress in this crucial area of AI development. Future work will explore how these findings translate to multimodal applications—a critical step towards truly conversational AI.