Can LLMs Compute with Reasons?

arXiv:2402.12080v1 Announce Type: new
Abstract: Large language models (LLMs) often struggle with complex mathematical tasks, prone to "hallucinating" incorrect answers due to their reliance on statistical patterns. This limitation is further amplified in average Small LangSLMs with limited context and training data. To address this challenge, we propose an "Inductive Learning" approach utilizing a distributed network of SLMs. This network leverages error-based learning and hint incorporation to refine the reasoning capabilities of SLMs. Our goal is to provide a framework …

abstract arxiv challenge compute context cs.cl data distributed inductive language language models large language large language models llms patterns reliance small statistical struggle tasks training training data type