Khái niệm cốt lõi
The author argues that reversing the digit order in arithmetic learning can significantly improve efficiency and accuracy, reducing complexity and training data requirements.
Tóm tắt
In the study, a novel approach is introduced to teach arithmetic to Large Language Models (LLMs) by prioritizing less significant digits first. This method, named LEFT (Little-Endian Fine-Tuning), outperformed previous state-of-the-art methods by 11.1% in accuracy while using only a fraction of the training tokens. The research delves into addition, subtraction, and multiplication tasks, showcasing how Little-Endian formatting simplifies computations and enhances model performance. By analyzing errors, attention weights, and performance trends with varying input digits, the study highlights the effectiveness and challenges of implementing LEFT in arithmetic learning for LLMs.
Thống kê
Compared to previous SOTA method, an overall improvement of 11.1% in accuracy was achieved.
Using LEFT required only a third of the tokens typically used during training.
In multiplication tasks, LEFT recorded a 35.7% performance gain while consuming only 56.6% of the training tokens used by prior SOTA.
Trích dẫn
"Reversing the number order enables models to better learn arithmetic operations."
"Our findings reveal an overall improvement of 11.1% in accuracy with LEFT."
"LEFT not only improves accuracy but also demonstrates efficiency by utilizing fewer training tokens."