Human Aligned Benchmark

Human-aligned benchmarks are designed to evaluate artificial intelligence models' performance on tasks relevant to human capabilities and needs, moving beyond purely technical metrics. Current research focuses on developing benchmarks for diverse tasks, including image generation, speech recognition (especially for children's speech), educational applications, and complex reasoning, often employing large language models (LLMs) and other deep learning architectures. These benchmarks are crucial for assessing the progress of AI systems towards human-level intelligence and for guiding the responsible development of AI technologies across various sectors, including education and healthcare. The ultimate goal is to create more reliable and useful AI systems that better serve human needs.