Reasoning Like Program Executors

• URL: http://arxiv.org/abs/2201.11473v1
• Date: Thu, 27 Jan 2022 12:28:24 GMT
• Title: Reasoning Like Program Executors
• Authors: Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Yan Gao, Qiang Fu, Jian-Guang Lou, Weizhu Chen
• Abstract summary: POET empowers language models to harvest the reasoning knowledge possessed in program executors via a data-driven approach. PoET can significantly boost model performance on natural language reasoning. PoET opens a new gate on reasoning-enhancement pre-training.
• Score: 48.819113224699976
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reasoning over natural language is a long-standing goal for the research community. However, studies have shown that existing language models are inadequate in reasoning. To address the issue, we present POET, a new pre-training paradigm. Through pre-training language models with programs and their execution results, POET empowers language models to harvest the reasoning knowledge possessed in program executors via a data-driven approach. POET is conceptually simple and can be instantiated by different kinds of programs. In this paper, we show three empirically powerful instances, i.e., POET-Math, POET-Logic, and POET-SQL. Experimental results on six benchmarks demonstrate that POET can significantly boost model performance on natural language reasoning, such as numerical reasoning, logical reasoning, and multi-hop reasoning. Taking the DROP benchmark as a representative example, POET improves the F1 metric of BART from 69.2% to 80.6%. Furthermore, POET shines in giant language models, pushing the F1 metric of T5-11B to 87.6% and achieving a new state-of-the-art performance on DROP. POET opens a new gate on reasoning-enhancement pre-training and we hope our analysis would shed light on the future research of reasoning like program executors.

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