[R] Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning

**Paper:** [**https://arxiv.org/pdf/2312.05571.pdf**](https://arxiv.org/pdf/2312.05571.pdf)

**Code:** [**https://github.com/joykirat18/SYRELM**](https://github.com/joykirat18/SYRELM)

**Abstract:** Large Language Models (LLM) exhibit zero-shot mathematical reasoning capacity as a behavior emergent with scale, commonly manifesting as chain-of-thoughts (CoT) reasoning. However, multiple empirical findings suggest that this prowess is exclusive to LLMs with exorbitant sizes (beyond 50 billion parameters). Meanwhile, educational neuroscientists suggest that symbolic algebraic manipulation be introduced around the same time as arithmetic word problems to modularize language-to-formulation, symbolic manipulation of the formulation, and endgame arithmetic. In this paper, we start with …

abstract behavior beyond billion capacity educational exclusive language language models large language large language models llm llms machinelearning manipulation mathematical reasoning multiple parameters reasoning scale thoughts word zero-shot