Training Large Language Models to be Better Rule Followers

• URL: http://arxiv.org/abs/2502.11525v1
• Date: Mon, 17 Feb 2025 07:54:50 GMT
• Title: Training Large Language Models to be Better Rule Followers
• Authors: Yi Hu, Shijia Kang, Haotong Yang, Haotian Xu, Muhan Zhang,
• Abstract summary: Large language models (LLMs) have shown impressive performance across a wide range of tasks.<n>Current training methods fail to leverage these rules effectively.<n>We propose Meta Rule-Following Fine-Tuning (Meta-RFFT) to enhance the cross-task transferability of rule-following abilities.
• Score: 23.958458849973248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have shown impressive performance across a wide range of tasks. However, they often exhibit unexpected failures in seemingly straightforward tasks, suggesting a reliance on case-based reasoning rather than rule-based reasoning. While the vast training corpus of LLMs contains numerous textual "rules", current training methods fail to leverage these rules effectively. Crucially, the relationships between these "rules" and their corresponding "instances" are not explicitly modeled. As a result, while LLMs can often recall rules with ease, they fail to apply these rules strictly and consistently in relevant reasoning scenarios. In this paper, we investigate the rule-following capabilities of LLMs and propose Meta Rule-Following Fine-Tuning (Meta-RFFT) to enhance the cross-task transferability of rule-following abilities. We first construct a dataset of 88 tasks requiring following rules, encompassing diverse reasoning domains. We demonstrate through extensive experiments that models trained on large-scale rule-following tasks are better rule followers, outperforming the baselines in both downstream fine-tuning and few-shot prompting scenarios. This highlights the cross-task transferability of models with the aid of Meta-RFFT. Furthermore, we examine the influence of factors such as dataset size, rule formulation, and in-context learning.

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