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arxiv:2305.14201

Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks

Published on May 23, 2023
· Submitted by akhaliq on May 23, 2023
#3 Paper of the day
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Abstract

We introduce Goat, a fine-tuned LLaMA model that significantly outperforms GPT-4 on a range of arithmetic tasks. Fine-tuned on a synthetically generated dataset, Goat achieves state-of-the-art performance on BIG-bench arithmetic sub-task. In particular, the zero-shot Goat-7B matches or even surpasses the accuracy achieved by the few-shot PaLM-540B. Surprisingly, Goat can achieve near-perfect accuracy on large-number addition and subtraction through supervised fine-tuning only, which is almost impossible with previous pretrained language models, such as Bloom, OPT, GPT-NeoX, etc. We attribute Goat's exceptional performance to LLaMA's consistent tokenization of numbers. To tackle more challenging tasks like large-number multiplication and division, we propose an approach that classifies tasks based on their learnability, and subsequently decomposes unlearnable tasks, such as multi-digit multiplication and division, into a series of learnable tasks by leveraging basic arithmetic principles. We thoroughly examine the performance of our model, offering a comprehensive evaluation of the effectiveness of our proposed decomposition steps. Additionally, Goat-7B can be easily trained using LoRA on a 24GB VRAM GPU, facilitating reproducibility for other researchers. We release our model, dataset, and the Python script for dataset generation.

Community

The GitHub link in the paper doesn’t put to a valid repo

Very interesting paper. Local running competitive model when it comes to arithmetic task.

Where's the dataset?

"Goat, a fine-tuned language model that is GOod at Arithmetic Tasks."

Lol love it

@winglian this one https://github.com/liutiedong/goat
its fine or something else you talking about ...

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