Reasoning Gap

The "reasoning gap" refers to the discrepancy between a large language model's (LLM) performance on individual tasks versus its ability to solve problems requiring multi-step reasoning or integrating information across multiple sources. Current research focuses on identifying and quantifying this gap across various domains, including mathematics, news commentary analysis, and music generation, using benchmarks that evaluate both the final answer and the intermediate reasoning steps. This research aims to improve LLMs' ability to perform complex reasoning tasks, impacting fields ranging from education and scientific discovery to more effective human-computer interaction. Understanding and mitigating this gap is crucial for developing more robust and reliable AI systems.