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Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate

Ziyang Huang, Wangtao Sun, Jun Zhao, Kang Liu

TL;DR

This work tackles the problem of rule retrieval for large language model reasoning by identifying semantic misalignment between concrete query facts and abstract rules. It introduces SIAR to induce self-derived rules that augment queries, and R^3 to re-estimate rule relevance for reranking retrieved rules. Across synthetic and real datasets, SIAR improves retrieval and reasoning over vanilla methods, and SIAR-R^3 yields further gains, with effects depending on rule format, retriever type, and model scale. The results demonstrate the practical potential of combining self-induced rule generation with relevance-based reranking to enhance rule-based reasoning in real-world applications.

Abstract

This paper systematically addresses the challenges of rule retrieval, a crucial yet underexplored area. Vanilla retrieval methods using sparse or dense retrievers to directly search for relevant rules to support downstream reasoning, often suffer from low accuracy. This is primarily due to a significant semantic gap between the instantiated facts in the queries and the abstract representations of the rules. Such misalignment results in suboptimal retrieval quality, which in turn negatively impacts reasoning performance. To overcome these challenges, we propose Self-Induction Augmented Retrieval (SIAR), a novel approach that utilizes Large Language Models (LLMs) to induce potential inferential rules that might offer benefits for reasoning by abstracting the underlying knowledge and logical structure in queries. These induced rules are then used for query augmentation to improve retrieval effectiveness. Additionally, we introduce Rule Relevance ReEstimate (R$^3$), a method that re-estimates the relevance of retrieved rules by assessing whether the abstract knowledge they contain can be instantiated to align with the facts in the queries and the helpfulness for reasoning. Extensive experiments across various settings demonstrate the effectiveness and versatility of our proposed methods.

Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate

TL;DR

This work tackles the problem of rule retrieval for large language model reasoning by identifying semantic misalignment between concrete query facts and abstract rules. It introduces SIAR to induce self-derived rules that augment queries, and R^3 to re-estimate rule relevance for reranking retrieved rules. Across synthetic and real datasets, SIAR improves retrieval and reasoning over vanilla methods, and SIAR-R^3 yields further gains, with effects depending on rule format, retriever type, and model scale. The results demonstrate the practical potential of combining self-induced rule generation with relevance-based reranking to enhance rule-based reasoning in real-world applications.

Abstract

This paper systematically addresses the challenges of rule retrieval, a crucial yet underexplored area. Vanilla retrieval methods using sparse or dense retrievers to directly search for relevant rules to support downstream reasoning, often suffer from low accuracy. This is primarily due to a significant semantic gap between the instantiated facts in the queries and the abstract representations of the rules. Such misalignment results in suboptimal retrieval quality, which in turn negatively impacts reasoning performance. To overcome these challenges, we propose Self-Induction Augmented Retrieval (SIAR), a novel approach that utilizes Large Language Models (LLMs) to induce potential inferential rules that might offer benefits for reasoning by abstracting the underlying knowledge and logical structure in queries. These induced rules are then used for query augmentation to improve retrieval effectiveness. Additionally, we introduce Rule Relevance ReEstimate (R), a method that re-estimates the relevance of retrieved rules by assessing whether the abstract knowledge they contain can be instantiated to align with the facts in the queries and the helpfulness for reasoning. Extensive experiments across various settings demonstrate the effectiveness and versatility of our proposed methods.
Paper Structure (33 sections, 2 figures, 6 tables)

This paper contains 33 sections, 2 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Preliminary
  3. Method
  4. SIAR: Self-Induction Augmented Retrieval
  5. R$^3$: Rule Relevance ReEstimate
  6. Experiment
  7. Retrieval Results and Discussion
  8. Open-source models have comparable performance with closed-source models.
  9. SIAR can consistently improve performance compared to vanilla retrieval.
  10. SIAR-R$^3$ can usually improve performance compared to SIAR.
  11. The performance difference of sparse retrieval and dense retrieval depends on the format of rule and dataset.
  12. Reasoning Results and Discussion
  13. Baselines
  14. Rules can effectively assist LLMs in reasoning, while directly retrieving rules for reasoning may lead to a decline in performance.
  15. SIAR and R$^3$ can boost the performance significantly.
  16. ...and 18 more sections

Figures (2)

  • Figure 1: (a) and (b) show the different characteristics of traditional knowledge retrieval and rule retrieval. (c) illustrates that the golden rule can significantly improve reasoning performance, while existing rule retrieval methods typically lead to a decline in reasoning performance due to suboptimal recall.
  • Figure 2: The workflow of retrieve-then-reason augmented with our method is shown in the middle of the Figure. To address the semantic misalignment issues, self-induction is first utilized to generate the hypothesized rule for query augmentation. Then, the new query is used for rule retrieval. And the retrieved rules are concatenated with the original query for reasoning. Building on this, we can reestimate the relevance of the rules with the query and improve the retrieval quality for better reasoning. The left bottom of the Figure shows the example of the self-induced rule. And the right bottom of the Figure shows the simplified reestimation prompt.