Multi Task Benchmark

Multi-task benchmarks evaluate the performance of machine learning models across multiple related tasks simultaneously, aiming to assess generalization ability and identify strengths and weaknesses in different model architectures. Current research focuses on developing more comprehensive and diverse benchmarks spanning various domains (e.g., natural language processing, computer vision, materials science), often incorporating realistic noise and distribution shifts to better reflect real-world scenarios. These benchmarks are crucial for advancing model development, facilitating fair comparisons between different approaches, and ultimately improving the robustness and applicability of machine learning in diverse fields.