PRIMO通过分阶段生成多跳开放规则，提升逻辑一致性与多样性。
PRIMO: Progressive Induction for Multi-hop Open Rule Generation
- 分三阶段生成规则：生成-提取-排序，逐步挖掘语言模型潜力
- 相比基线模型，规则质量与多样性显著提升，原子重复率降低
- 融合本体信息与人类反馈强化学习，增强常识理解能力
开放规则指从前提原子推导出结论原子的蕴含关系，捕捉现实世界中实例间的多种关联。将开放规则知识注入机器可提升对话和关系抽取等下游任务性能。现有方法仅关注单跳规则生成，忽视多跳场景，导致前提与结论原子间逻辑不一致，以及生成规则原子语义重复。为此，我们提出一种渐进式多阶段开放规则生成方法PRIMO。在规则生成阶段引入本体信息以减少歧义、提升准确性。PRIMO构建包含生成、提取与排序模块的多阶段结构，从多个维度充分挖掘语言模型中的潜在知识。此外，采用基于人类反馈的强化学习进一步优化模型，增强其对常识知识的理解。实验表明，相比基线模型，PRIMO显著提升规则质量与多样性，同时降低规则原子重复率。
原文摘要 · Abstract (English)
Open rule refer to the implication from premise atoms to hypothesis atoms, which captures various relations between instances in the real world. Injecting open rule knowledge into the machine helps to improve the performance of downstream tasks such as dialogue and relation extraction. Existing approaches focus on single-hop open rule generation, ignoring multi-hop scenarios, leading to logical inconsistencies between premise and hypothesis atoms, as well as semantic duplication of generated rule atoms. To address these issues, we propose a progressive multi-stage open rule generation method called PRIMO. We introduce ontology information during the rule generation stage to reduce ambiguity and improve rule accuracy. PRIMO constructs a multi-stage structure consisting of generation, extraction, and ranking modules to fully leverage the latent knowledge within the language model across multiple dimensions. Furthermore, we employ reinforcement learning from human feedback to further optimize model, enhancing the model's understanding of commonsense knowledge. Experiments show that compared to baseline models, PRIMO significantly improves rule quality and diversity while reducing the repetition rate of rule atoms.
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