Explaining the Complex Task Reasoning of Large Language Models with Template-Content Structure

Haotong Yang, Fanxu Meng, Zhouchen Lin, Muhan Zhang

Primary Area: visualization or interpretation of learned representations

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Keywords: large language model, complex task reasoning, template-content structure, autoregressive model

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TL;DR: We propose a template-content structure to explain the complex task reasoning capacity of LLMs.

Abstract: The continuous evolution of pre-trained large language models with ever-growing parameters and corpus sizes has augmented their capacity to solve complex tasks. This ability, which obviates the necessity for task-specific training or fine-tuning, relies on providing the model with a language description or some task exemplars---referred to the *prompt*---that guide the desired autoregressive generation. Despite the remarkable success, the underlying mechanisms that facilitate such exceptional generalization abilities remain an open question. In this paper, we present a novel framework that formally conceptualizes answer generation for complex natural language tasks as a hierarchical *''template-content''* structure. According to our modeling, there exist pre-trained models that can automatically decompose tasks into constituent steps during autoregressive generation, through language modeling on a sufficiently large corpus, thereby solving them. Our framework offers an explanatory tool for the complex reasoning abilities of large language models from the perspective of modeling autoregressive generation tasks. Our experiments show that real-world models exhibit distinct behaviors for ''template'' and ''content'', providing support for our modeling.

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Submission Number: 5116