Brain Teaser

Brain teaser research focuses on evaluating and enhancing the ability of large language models (LLMs) to solve puzzles requiring lateral thinking and unconventional reasoning, often using multiple-choice question answering formats. Current research employs various prompting techniques, including few-shot learning and model-generated reasoning strategies, within transformer-based architectures to improve LLM performance on both sentence and word puzzles. These studies highlight the limitations of current LLMs in complex reasoning tasks while also demonstrating significant progress in bridging the gap between machine and human performance on certain types of brain teasers. This work contributes to a deeper understanding of LLM cognitive abilities and has implications for improving AI problem-solving capabilities across diverse domains.