Towards Understanding How Transformer Perform Multi-step Reasoning with Matching Operation
- URL: http://arxiv.org/abs/2405.15302v1
- Date: Fri, 24 May 2024 07:41:26 GMT
- Title: Towards Understanding How Transformer Perform Multi-step Reasoning with Matching Operation
- Authors: Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu,
- Abstract summary: Investigating internal reasoning mechanisms of large language models can help us design better model architectures and training strategies.
We investigate the matching mechanism employed by Transformer for multi-step reasoning on a constructed dataset.
We propose a conjecture on the upper bound of the model's reasoning ability based on this phenomenon.
- Score: 52.77133661679439
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models have consistently struggled with complex reasoning tasks, such as mathematical problem-solving. Investigating the internal reasoning mechanisms of these models can help us design better model architectures and training strategies, ultimately enhancing their reasoning capabilities. In this study, we examine the matching mechanism employed by Transformer for multi-step reasoning on a constructed dataset. We investigate factors that influence the model's matching mechanism and discover that small initialization and post-LayerNorm can facilitate the formation of the matching mechanism, thereby enhancing the model's reasoning ability. Moreover, we propose a method to improve the model's reasoning capability by adding orthogonal noise. Finally, we investigate the parallel reasoning mechanism of Transformers and propose a conjecture on the upper bound of the model's reasoning ability based on this phenomenon. These insights contribute to a deeper understanding of the reasoning processes in large language models and guide designing more effective reasoning architectures and training strategies.
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