# **Critique Of “Humanity’s Last Exam”** 1. **Overly Constructed Questions** HLE relies on hyper-specialized terminology and niche academic questions (e.g., hummingbird anatomy or Biblical Hebrew syllable analysis) that lack real-world applicability . These questions test memorization of obscure facts rather than generalizable reasoning, which limits their utility for evaluating AI’s practical problem-solving abilities. For example, a question about the number of tendons in a hummingbird’s sesamoid bone is irrelevant to most real-world AI applications . 2. **Lack of Empirical Validation** - **No Human Baseline**: HLE does not report how expert humans perform on the test, making it impossible to contextualize AI scores. If even experts struggle, the benchmark may reflect inherent flaws in question design rather than AI limitations . - **Static Benchmark**: Unlike adaptive tests like the SAT, which evolve to reflect educational trends, HLE is static. AI models improve rapidly (e.g., MATH benchmark scores rose from <10% to >90% in 3 years ), so a fixed test risks obsolescence. 3. **Narrow Focus on Closed-Ended Tasks** HLE prioritizes unambiguous, academic-style answers, ignoring open-ended reasoning, creativity, and ethical judgment—critical skills for real-world AI applications like medical diagnosis or legal analysis . --- ## **Designing A Better Benchmark** To address these flaws, a new benchmark should prioritize **real-world relevance**, **empirical rigor**, and **adaptability**. Here’s a proposed framework: ### **1. Dynamic, Evolving Questions** - **Rotating Question Bank**: Regularly update questions to reflect advancements in AI and human knowledge, preventing benchmark saturation . - **Community Contributions**: Allow domain experts and users to submit questions based on emerging challenges (e.g., climate modeling, AI ethics dilemmas) . - **Longitudinal Tracking**: Compare AI performance against human baselines over time to measure progress meaningfully . ### **2. Real-World Task Simulation** - **Multi-Modal Scenarios**: Include tasks like interpreting medical imaging, negotiating contracts, or troubleshooting code, requiring integration of text, images, and contextual reasoning . - **Ambiguity and Uncertainty**: Test how models handle incomplete information (e.g., diagnosing a patient with vague symptoms) . - **Ethical and Creative Challenges**: Evaluate responses to dilemmas (e.g., resource allocation during crises) or creative tasks (e.g., designing sustainable infrastructure) . ### **3. Empirical Validation** - **Human Performance Baselines**: Collect data from experts, laypersons, and domain professionals to establish score ranges. For example, if humans score 40% on a task and AI scores 35%, the gap is clearer . - **Cross-Cultural and Interdisciplinary Input**: Ensure questions reflect diverse global perspectives to avoid Western-centric or niche biases . ### **4. Transparency and Anti-Overfitting Measures** - **Public and Private Subsets**: Release a public dataset for training and a private set for evaluation to prevent model overfitting . - **Explainability Metrics**: Require models to justify answers (e.g., “Show your work”), assessing not just correctness but reasoning transparency . ### **5. Integration with Practical Use Cases** - **Industry Partnerships**: Collaborate with sectors like healthcare, education, and engineering to design tasks mirroring real challenges (e.g., optimizing supply chains, tutoring students) . - **User-Centric Evaluation**: Include feedback from end-users (e.g., doctors, teachers) to assess AI’s utility in workflows . --- ## **Example: A Real-World Benchmark Task** **Task**: *Design a disaster relief plan for a flood-prone region using real-time weather data, budget constraints, and community demographics.* - **Evaluation Criteria**: - Accuracy of risk predictions. - Ethical allocation of resources. - Adaptability to new data (e.g., sudden rainfall changes). - Clarity of communication for non-experts . --- ## **Conclusion** HLE highlights the limitations of current AI benchmarks but falls short in addressing real-world complexity. A better benchmark must balance academic rigor with practical relevance, adapt to technological progress, and incorporate human baselines. By focusing on dynamic, interdisciplinary tasks and empirical validation, we can create a tool that truly measures AI’s readiness for societal challenges.