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ArthModel: Enhance Arithmetic Skills to Large Language Model

arXiv 2311.18609 ↗First posted 30 Nov 2023cs.CLcited by 0

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Abstract (arXiv)

With the great success of ChatGPT, the research of large language models has become increasingly popular. However, the models have several limitations, such as toxicity and pool performance of arithmetic solving. Meanwhile, LLM may have some potential abilities that have yet to be exploited. In this paper, we choose a different way to enhance the arithmetic ability of LLM. We propose to train LLM to generate a postfix expression related to the arithmetic problem and incorporate it with small pretrained models. Moreover, this small model transfers the token embeddings into real dense numbers and invokes native functions of a deep learning platform to get the correct answer. To generate the final result, we propose prompt injection for adding the result outputs by the small model to LLM. This work provides different ways of thinking, training and using a language model. The codes and models will be released at urlhttps://github.com/eteced/arithmeticfinetuningv1.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 4 references that are in the map. First posted on arXiv 30 Nov 2023.

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References · 4

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,125
  4. 2022PAL: Program-aided Language Modelscited by 652

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  8. 2024Arithmetic Reasoning with LLM: Prolog Generation & Permutation