The field of Math-NLP has witnessed significant growth in recent years,
motivated by the desire to expand LLM performance to the learning of
non-linguistic notions (numerals, and subsequently, arithmetic reasoning).
However, non-linguistic skill injection typically comes at a cost for LLMs: it
leads to catastrophic forgetting of core linguistic skills, a consequence that
often remains unaddressed in the literature. As Math-NLP has been able to
create LLMs that can closely approximate the mathematical skills of a
grade-schooler or the arithmetic reasoning skills of a calculator, the
practicality of these models fail if they concomitantly shed their linguistic
capabilities. In this work, we take a closer look into the phenomena of
catastrophic forgetting as it pertains to LLMs and subsequently offer a novel
framework for non-linguistic skill injection for LLMs based on information
theoretic interventions and skill-specific losses that enable the learning of
strict arithmetic reasoning. Our model outperforms the state-of-the-art both on
injected non-linguistic skills and on linguistic knowledge retention, and does
so with a fraction of the non-linguistic training data (1/4) and zero
additional synthetic linguistic training data.

本文提出了一种基于信息论干预和特定技能损失的新型非语言技能注入框架，可使 LLMs 学习严格的算术推理，相比注入非语言技能和保持语言知识的现有技术，我们的模型在使用少量数据且不产生额外合成语言训练数据的情况下表现更好。