We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| thudm/mathglm | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| get_masks_and_position_ids | Ran | thudm/mathglm/MathGLM_Arithmetic/pretrain_mathglm.py pointer only (licence: NONE) · get_code("1e7fe182da8a9cdd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Previous studies have typically assumed that large language models are unable to accurately perform arithmetic operations, particularly multiplication of >8 digits, and operations involving decimals and fractions, without the use of calculator tools. This paper aims to challenge this misconception. With sufficient training data, a 2 billion-parameter language model can accurately perform multi-digit arithmetic operations with almost 100% accuracy without data leakage, significantly surpassing GPT-4 (whose multi-digit multiplication accuracy is only 4.3%). We also demonstrate that our MathGLM, fine-tuned from GLM-10B on a dataset with additional multi-step arithmetic operations and math problems described in text, achieves similar performance to GPT-4 on a 5,000-samples Chinese math problem test set. Our code and data are public at https://github.com/THUDM/MathGLM.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2309.03241")
get_code_for_paper("2309.03241")
have("2309.03241")
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