Compositional Problem

The compositional problem in artificial intelligence focuses on the challenge of enabling AI models to effectively combine learned concepts to generate novel outputs, mirroring human cognitive abilities. Current research investigates this limitation across various modalities, including text-to-image generation, language understanding, and mathematical reasoning, often employing large language models (LLMs) and diffusion models. Researchers are actively exploring techniques like prompt engineering, fine-tuning, and architectural modifications to improve compositional capabilities, aiming to enhance the robustness and generalizability of AI systems. Addressing this problem is crucial for advancing AI's ability to handle complex, unseen situations and ultimately building more versatile and human-like intelligent systems.