SkyMath: Technical Report

Liu Yang, Haihua Yang, Wenjun Cheng, Lei Lin, Chenxia Li +12 more
10/25/2023
cs.CLcs.AI

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

Large language models (LLMs) have shown great potential to solve varieties of natural language processing (NLP) tasks, including mathematical reasoning. In this work, we present SkyMath, a large language model for mathematics with 13 billion parameters. By applying self-compare fine-tuning, we have enhanced mathematical reasoning abilities of Skywork-13B-Base remarkably. On GSM8K, SkyMath outperforms all known open-source models of similar size and has established a new SOTA performance.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

92% match1 repo4552023

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

92% match1 repo4552023

Large Language Models for Mathematical Reasoning: Progresses and Challenges

90% match2024

ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving

89% match2023

ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving

89% match1 repo1.1k2023

MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning

89% match1 repo3832023

Cite this paper

@article{yang2023skymath,
  title  = {SkyMath: Technical Report},
  author = {Liu Yang and Haihua Yang and Wenjun Cheng and Lei Lin and Chenxia Li and Yifu Chen and Lunan Liu and Jianfei Pan and Tianwen Wei and Biye Li and Liang Zhao and Lijie Wang and Bo Zhu and Guoliang Li and Xuejie Wu and Xilin Luo and Rui Hu},
  year   = {2023},
  eprint = {2310.16713},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.16713v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2310.16713.svg)](https://codeofpaper.com/paper/2310.16713)

Discussion