Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs (2407.00653v1)

Abstract: LLMs have exhibited impressive proficiency in various NLP tasks, which involve increasingly complex reasoning. Knowledge reasoning, a primary type of reasoning, aims at deriving new knowledge from existing one.While it has been widely studied in the context of knowledge graphs (KGs), knowledge reasoning in LLMs remains underexplored. In this paper, we introduce Chain-of-Knowledge, a comprehensive framework for knowledge reasoning, including methodologies for both dataset construction and model learning. For dataset construction, we create KnowReason via rule mining on KGs. For model learning, we observe rule overfitting induced by naive training. Hence, we enhance CoK with a trial-and-error mechanism that simulates the human process of internal knowledge exploration. We conduct extensive experiments with KnowReason. Our results show the effectiveness of CoK in refining LLMs in not only knowledge reasoning, but also general reasoning benchmarkms.

Chain-of-Knowledge: Integrating Knowledge Reasoning into LLMs by Learning from Knowledge Graphs

LLMs have demonstrated remarkable capabilities across a variety of NLP tasks, including complex reasoning challenges such as arithmetic, commonsense, and symbolic reasoning. However, the domain of knowledge reasoning—deriving new knowledge from existing knowledge—has remained relatively unexplored within LLMs compared to its extensive paper in the context of Knowledge Graphs (KGs). This paper proposes and evaluates Chain-of-Knowledge (CoK), a framework specifically designed to imbue LLMs with robust knowledge reasoning abilities leveraging KGs.

Methodology

The CoK framework encompasses both dataset construction and model learning methodologies. For dataset construction, a structured process is employed:

1. Rule Mining: Initially, rules are mined from KGs through a breadth-first search method to extract 2-hop relations, and subsequently extended to 3-hop and 4-hop rules by combining shorter rules.
2. Knowledge Selection: Ensuring that the selected knowledge is representative and does not lead to overfitting involves both anonymized and regular settings. Anonymized settings avoid data leakage by replacing entities with random strings, while regular settings check the model’s internal knowledge to ensure genuine reasoning evaluation.
3. Sample Generation: Advanced LLMs are used to transform KGs into natural language, forming the basis of the CoK dataset.

For model learning, two primary methodologies are outlined:

• Behavior Cloning: Training LLMs directly on the CoK dataset, which often leads to rule overfitting and hallucination.
• Trial-and-Error Mechanism: Enhances generalization by simulating human knowledge exploration and backtracking when incomplete or inaccurate information is used in reasoning.

Experiments

The KnowReason dataset, developed in this paper, serves as the experimental bedrock, containing a meticulously gathered set of rules and samples for both anonymized and regular settings. The experiments evaluate LLMs on knowledge reasoning abilities within these settings and include in-domain (ID) tests, where reasoning paths match those in training, and out-of-domain (OOD) tests, involving unseen rules.

Results indicate that the CoK and CoK (Trial-and-Error) frameworks outperform baseline methods, with CoK (Trial-and-Error) particularly excelling in OOD tests, thereby demonstrating improved generalization and reduced rule dependency. Quantitative results further highlight the CoK framework's efficacy: substantial improvements in reasoning tasks—general and domain-specific. Also, CoK is validated on public benchmarks like CommonsenseQA, ARC, and BBH, exhibiting better performance than vanilla LLMs and ICL-CoK methods.

Implications and Future Work

The implications of this research are multifaceted. Practically, the CoK framework can significantly improve the utility of LLMs in domains where complex and multi-hop reasoning over knowledge bases is essential, such as legal reasoning, medical diagnostics, and advanced customer support. Theoretically, it opens the door for more nuanced integration of symbolic reasoning methods into LLMs, potentially inspiring hybrid models that synergize symbolic and sub-symbolic AI paradigms effectively.

Speculative future developments might include extending the CoK framework to incorporate dynamic updating mechanisms for KGs, further enhancing the adaptability and relevance of LLMs in real-time applications. Additionally, exploration into optimizing the trial-and-error mechanism to minimize computational overhead while maximizing reasoning accuracy would be another promising direction.

Conclusion

This paper articulates a comprehensive approach to integrating knowledge reasoning into LLMs through the Chain-of-Knowledge framework. By systematically constructing the KnowReason dataset and implementing advanced learning techniques, it achieves notable advancements in LLM performance across knowledge reasoning tasks. While addressing the current limitations regarding evaluation benchmarks and data specificity, this research sets a foundational approach for future endeavors in enhancing LLM capabilities for knowledge-intensive applications.