Calc-X: Enriching Arithmetical Chain-of-Thoughts Datasets by Interaction with Symbolic Systems
Abstract
The report discusses enhancing chain-of-thought datasets with non-parametric components and proposes a HTML-like format to integrate large language models and symbolic systems for better arithmetical reasoning.
This report overviews our ongoing work in enriching chain-of-thoughts datasets requiring arithmetical reasoning with the integration of non-parametric components, such as a calculator. We conduct an analysis of prominent relevant datasets such as GSM8K, Ape210K, AQuA-RAT, and MathQA and propose a machine-processable HTML-like format specifically tailored for working with semi-structured chains. By converting the datasets into this unified format, we enable the effective integration of large language models and symbolic systems, empowering them to tackle arithmetical reasoning tasks more efficiently.
Models citing this paper 5
Browse 5 models citing this paperDatasets citing this paper 8
Browse 8 datasets citing this paperSpaces citing this paper 1
Collections including this paper 0
No Collection including this paper