Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA
Maharshi Gor, Hal Daumé, Tianyi Zhou, Jordan Lee Boyd-Graber · 2024
Recent advancements of large language models (LLMs) have led to claims of AI surpassing humans in natural language processing (NLP) tasks such as textual understanding and reasoning.This work investigates these assertions by introducing CAIMIRA, a novel framework rooted in item response theory (IRT) that enables quantitative assessment and comparison of problem-solving abilities in questionanswering (QA) agents.Through analysis of over 300,000 responses from ~70 AI systems and 155 humans across thousands of quiz questions, CAIMIRA uncovers distinct proficiency patterns in knowledge domains and reasoning skills.Humans outperform AI systems in knowledge-grounded abductive and conceptual reasoning, while state-of-the-art LLMs like GPT-4-TURBO and LLAMA-3-70B demonstrate superior performance on targeted information retrieval and fact-based reasoning, particularly when information gaps are well-defined and addressable through pattern matching or data retrieval.These findings identify key areas for future QA tasks and model development, highlighting the critical need for questions that not only challenge higher-order reasoning and scientific thinking, but also demand nuanced linguistic and cross-contextual application.