Summary of Do Great Minds Think Alike? Investigating Human-ai Complementarity in Question Answering with Caimira, by Maharshi Gor et al.
Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA
by Maharshi Gor, Hal Daumé III, Tianyi Zhou, Jordan Boyd-Graber
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in large language models (LLMs) have sparked claims of AI surpassing humans in natural language processing (NLP) tasks. This paper investigates these assertions by introducing CAIMIRA, a novel framework rooted in item response theory (IRT), which enables quantitative assessment and comparison of problem-solving abilities between human and AI question-answering agents. The study analyzes over 300,000 responses from ~70 AI systems and 155 humans across thousands of quiz questions, revealing distinct proficiency patterns in knowledge domains and reasoning skills. While humans outperform AI systems in knowledge-grounded abductive and conceptual reasoning, state-of-the-art LLMs like GPT-4 and LLaMA show superior performance on targeted information retrieval and fact-based reasoning, especially when information gaps are well-defined and addressable through pattern matching or data retrieval. This research highlights the need for future QA tasks to focus on questions that challenge not only higher-order reasoning and scientific thinking but also demand nuanced linguistic interpretation and cross-contextual knowledge application, helping advance AI developments that better emulate or complement human cognitive abilities in real-world problem-solving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well artificial intelligence (AI) systems can understand and answer questions compared to humans. The researchers created a new way to measure this called CAIMIRA. They analyzed over 300,000 answers from AI systems and humans on thousands of quiz questions and found that humans are better at answering certain types of questions that require higher-level thinking and understanding. On the other hand, AI systems like GPT-4 and LLaMA are good at finding specific facts and information. The study suggests that we need to create more challenging questions for AI systems to test their abilities in a way that’s similar to how humans think and solve problems. |
Keywords
» Artificial intelligence » Gpt » Llama » Natural language processing » Nlp » Pattern matching » Question answering
