Can LLMs Compute with Reasons?
Harshit Sandilya Peehu Raj Jainit Sushil Bafna Srija Mukhopadhyay Shivansh Sharma Ellwil Sharma Arastu Sharma Neeta Trivedi Manish Shrivastava Rajesh Kumar
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 that
empowers SLMs to approach the level of logic-based applications achieved by
high-parameter models, potentially benefiting any language model. Ultimately,
this novel concept paves the way for bridging the logical gap between humans
and LLMs across various fields.