Self-supervised Analogical Learning using Language Models

• URL: http://arxiv.org/abs/2502.00996v1
• Date: Mon, 03 Feb 2025 02:31:26 GMT
• Title: Self-supervised Analogical Learning using Language Models
• Authors: Ben Zhou, Sarthak Jain, Yi Zhang, Qiang Ning, Shuai Wang, Yassine Benajiba, Dan Roth,
• Abstract summary: We propose SAL, a self-supervised analogical learning framework. SAL mimics the human analogy process and trains models to explicitly transfer high-quality symbolic solutions. We show that the resulting models outperform base language models on a wide range of reasoning benchmarks.
• Score: 59.64260218737556
• License:
• Abstract: Large language models have been shown to suffer from reasoning inconsistency issues. That is, they fail more in situations unfamiliar to the training data, even though exact or very similar reasoning paths exist in more common cases that they can successfully solve. Such observations motivate us to propose methods that encourage models to understand the high-level and abstract reasoning processes during training instead of only the final answer. This way, models can transfer the exact solution to similar cases, regardless of their relevance to the pre-training data distribution. In this work, we propose SAL, a self-supervised analogical learning framework. SAL mimics the human analogy process and trains models to explicitly transfer high-quality symbolic solutions from cases that they know how to solve to other rare cases in which they tend to fail more. We show that the resulting models after SAL learning outperform base language models on a wide range of reasoning benchmarks, such as StrategyQA, GSM8K, and HotpotQA, by 2% to 20%. At the same time, we show that our model is more generalizable and controllable through analytical studies.

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