Analogical Reasoning

Analogical reasoning, the ability to identify parallels between disparate situations and apply learned solutions to novel problems, is a key area of research in artificial intelligence, focusing on how to imbue large language models (LLMs) and other AI systems with this human-like capacity. Current research investigates how different model architectures, including those leveraging reinforcement learning, knowledge graphs, and contrastive learning, can improve LLMs' performance on various analogical reasoning tasks, such as solving visual analogies, processing noisy speech, and making decisions under uncertainty. This research is significant because improved analogical reasoning capabilities in AI could lead to more robust and adaptable systems across diverse applications, from medical diagnosis to scientific discovery.