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Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics
Summary
This research investigates how large language models (LLMs) perform arithmetic tasks. Instead of using complex algorithms or memorizing training data, the authors discovered that LLMs rely on a "bag of heuristics". These heuristics are simple rules or patterns learned from the training data that are applied to specific numerical inputs. The study shows that these heuristics emerge gradually during the model's training process and are the primary mechanism for arithmetic reasoning even in early stages.
原文链接：https://arxiv.org/abs/2410.21272
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