Specializing Smaller Language Models towards Multi-Step Reasoning

• URL: http://arxiv.org/abs/2301.12726v1
• Date: Mon, 30 Jan 2023 08:51:19 GMT
• Title: Specializing Smaller Language Models towards Multi-Step Reasoning
• Authors: Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal and Tushar Khot
• Abstract summary: We show that abilities can be distilled down from GPT-3.5 ($ge$ 175B) to T5 variants ($le$ 11B) We propose model specialization, to specialize the model's ability towards a target task.
• Score: 56.78474185485288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The surprising ability of Large Language Models (LLMs) to perform well on complex reasoning with only few-shot chain-of-thought prompts is believed to emerge only in very large-scale models (100+ billion parameters). We show that such abilities can, in fact, be distilled down from GPT-3.5 ($\ge$ 175B) to T5 variants ($\le$ 11B). We propose model specialization, to specialize the model's ability towards a target task. The hypothesis is that large models (commonly viewed as larger than 100B) have strong modeling power, but are spread on a large spectrum of tasks. Small models (commonly viewed as smaller than 10B) have limited model capacity, but if we concentrate their capacity on a specific target task, the model can achieve a decent improved performance. We use multi-step math reasoning as our testbed because it is a very typical emergent ability. We show two important aspects of model abilities: (1). there exists a very complex balance/ tradeoff between language models' multi-dimensional abilities; (2). by paying the price of decreased generic ability, we can clearly lift up the scaling curve of models smaller than 10B towards a specialized multi-step math reasoning ability. We further give comprehensive discussions about important design choices for better generalization, including the tuning data format, the start model checkpoint, and a new model selection method. We hope our practice and discoveries can serve as an important attempt towards specialized smaller models in the new research paradigm set by LLMs.

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