****** Chatbot and Human Intelligence Benchmarks ****** https://g.co/gemini/share/ac22aee05ded Creato con Gemini 2.5 Flash 5 ottobre 2025 alle ore 18:28 (CEST) Data pubblicazione: 5 ottobre 2025 alle ore 18:38 (CEST) ================================================================================ -- HO -------------------------------------------------------------------------- Which are the benchmarks that can be used to rate chatbots and humans as well (for example SimpleQA, Diamond, etc). Among these tests which ones you think are related to intelligent in humans terms rather than information or speed in calculating? For example IQ test seems more related to a sort of pattern recognition rather than a problem solving attitude. While Mensa and higher IQ test are more problem solving oriented. SimpleQA is seem a matter of notion but that question are so delicate that notions are not enough for achieving a high score but understanding. -- AI -------------------------------------------------------------------------- The benchmarks used to rate both chatbots and humans are generally those designed to test advanced knowledge, reasoning, and problem-solving skills across various domains. ***** Benchmarks for Chatbot and Human Evaluation ***** Benchmark Focus Human Performance Context GPQA Diamond Highly specialized and complex multiple-choice questions in biology, physics, (Graduate- and chemistry. The "Diamond" subset is specifically designed to be difficult Assesses expert-level knowledge and reasoning Level Google- even with internet access. (e.g., PhD-level experts scored ≈65%−74%). Proof Q&A) MMLU (Massive Measures general knowledge and reasoning Multitask Tests an extensive range of subject knowledge across ∼57 diverse academic and abilities comparable to human performance on Language professional fields (e.g., law, ethics, mathematics). real-world exams. Understanding) Measures factual accuracy and reliability by answering short, clear, fact-based Evaluates a baseline of general factual SimpleQA questions. knowledge and the ability to retrieve precise information. Focuses on a core element of human ARC-AGI (AI2 Tests generalization and novel problem-solving through visual-spatial pattern intelligence: the ability to learn from a Reasoning recognition and transformation problems (like an IQ test's matrix reasoning). small number of examples (sample efficiency) Challenge-AGI) and apply that learning to new, unseen problems. Evaluates conversational problem-solving and how a user and an AI can Measures interactive intelligence and how ChatBench collaborate to answer complex questions (a "human-AI" evaluation). human-AI teams compare to human-alone or AI- alone performance. ***** Benchmarks Related to "Human Intelligence" ***** Based on your distinction between pattern recognition/notions and problem-solving attitude or understanding, the benchmarks that best align with a human-like, deep intelligence are those that require complex reasoning, synthesis of information, and generalization beyond rote memorization or simple fact retrieval. **** 1. GPQA Diamond 💎 (Graduate-Level Reasoning) **** This benchmark is arguably the most demanding and is highly correlated with human-like deep reasoning and understanding. * Why it aligns with intelligence: The questions are specifically crafted by domain experts to be "Google-Proof". This means simply searching for facts isn't enough; an agent (human or machine) must synthesize knowledge, apply complex scientific principles, and reason through multi-step logic to arrive at the correct answer. The low score of non-experts, even with web access, highlights that pure information retrieval is insufficient—true understanding of the concepts is required. **** 2. ARC-AGI (Generalization and Adaptation) **** This test is designed to measure a fundamental aspect of general intelligence, which is crucial for a human-like mind. * Why it aligns with intelligence: It doesn't test what you know, but how you learn. It assesses sample efficiency and the capacity to generalize rules from a few examples to solve a novel, abstract problem. This ability to adapt and apply learned concepts to unseen situations is a hallmark of fluid human intelligence, as opposed to crystallized knowledge or computational speed. **** 3. MMLU (Conceptual Knowledge and Contextual Reasoning) **** While it covers factual domains, the vast breadth and complexity of the questions require more than simple memorization. * Why it aligns with intelligence: A high score indicates a broad, integrated understanding of multiple fields, including philosophy, ethics, and high-level subjects. Answering these questions correctly often requires contextual reasoning and selecting the best concept among subtle choices, which tests a model's ability to handle nuance and subject-specific logic. **** On SimpleQA and the Notion of Understanding **** You are correct that SimpleQA (Simple Question Answering) is fundamentally a matter of notion/fact retrieval. However, as you observe, a high score can suggest a deeper quality: * Understanding vs. Notion: While the questions are factual, the challenge for large language models (LLMs) is ensuring reliability and accuracy (avoiding hallucinations), which is not a trivial task even with vast data. This requires the model to know not just facts, but which facts are correct and to handle delicate details—a kind of fact-based precision that moves beyond simple recall toward a more "verified" understanding of a knowledge graph. However, compared to GPQA or ARC-AGI, its primary focus remains on knowledge retrieval rather than complex, novel problem-solving.