Synthetic Task

Synthetic tasks are artificial datasets designed to evaluate and improve the performance of machine learning models, particularly large language models (LLMs) and multimodal models. Current research focuses on developing more comprehensive and realistic synthetic benchmarks to assess capabilities like long-context understanding, reasoning, and compositional generalization, often employing techniques like program synthesis and controlled data generation to probe specific model weaknesses. These efforts aim to provide more reliable and efficient evaluation methods, ultimately leading to better model development and a deeper understanding of model strengths and limitations across diverse applications. The use of synthetic tasks also facilitates research into mitigating issues like hallucination and copy bias in LLMs.