Enhancing Complex Logical Reasoning over Knowledge Graphs with Logic-Aware Curriculum Tuning

المفاهيم الأساسية

A novel fine-tuning framework, Logic-Aware Curriculum Tuning (LACT), is proposed to enhance the complex logical reasoning ability of large language models over knowledge graphs by incorporating logical context and leveraging curriculum learning.

الملخص

The paper presents a novel framework called Logic-Aware Curriculum Tuning (LACT) to improve the complex logical reasoning ability of large language models (LLMs) over knowledge graphs (KGs).
The key highlights are:

LACT incorporates logical context into the fine-tuning corpus by leveraging binary tree decomposition to transform complex first-order logic (FOL) queries into a sequence of simpler subqueries. This stimulates the LLM's ability to decompose and reason over complex logical structures.

LACT employs a curriculum learning strategy to smooth the difficulty gap among different types of complex queries, gradually exposing the LLM to more challenging reasoning tasks.

Extensive experiments demonstrate that LACT significantly outperforms previous embedding-based and PLM-based methods, achieving new state-of-the-art performance on widely used datasets like FB15K, FB15K-237 and NELL995.

Further analyses reveal that LACT's performance is correlated with the completeness of relevant information extracted from the KG and the complexity of the logical queries. LACT shows stronger improvements on more challenging reasoning tasks.

Case studies illustrate how LACT can leverage the provided KG context to perform step-by-step logical reasoning, in contrast to the limitations of pure prompt-based approaches.

Overall, the LACT framework effectively enhances the complex logical reasoning capabilities of LLMs by incorporating logical structure and leveraging curriculum learning, demonstrating the benefits of combining knowledge graphs and large language models.

الإحصائيات

The set of entities E is connected to entity Africa by relation adjoins.
The set of entities F is connected to entity Beckham by relation owner.
The set of entities G is the intersection of sets E and F.

اقتباسات

None

الرؤى الأساسية المستخلصة من

Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning

by Tianle Xia,L... في arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01649.pdf

استفسارات أعمق

How can the LACT framework be extended to handle more diverse types of logical queries beyond the ones considered in this work?

The LACT framework can be extended to handle more diverse types of logical queries by incorporating additional logical operators and query structures. Currently, LACT focuses on First-Order Logic (FOL) queries with projection, conjunction, disjunction, and negation operators. To handle more diverse queries, the framework can be expanded to include higher-order logic, existential quantifiers, universal quantifiers, and other complex logical operators. By incorporating a wider range of logical structures, LACT can enhance its reasoning capabilities for a broader set of queries.

What are the potential limitations of the binary tree decomposition strategy, and how could it be further improved to handle more complex logical structures?

One potential limitation of the binary tree decomposition strategy is its scalability to handle extremely complex logical structures with multiple nested operations and dependencies. As the complexity of the logical query increases, the binary tree decomposition may become less effective in capturing all the intricate relationships and dependencies within the query.
To address this limitation and improve the handling of more complex logical structures, the binary tree decomposition strategy could be enhanced in the following ways:

Multi-level Decomposition: Introduce a multi-level decomposition approach where the query is decomposed into multiple layers of binary trees, allowing for a more granular breakdown of the logical structure.
Adaptive Decomposition: Develop an adaptive decomposition algorithm that dynamically adjusts the decomposition process based on the complexity of the query, ensuring that all relevant relationships are captured.
Hierarchical Decomposition: Implement a hierarchical decomposition method that organizes the logical operations in a hierarchical manner, enabling a more structured and comprehensive breakdown of the query.

By incorporating these enhancements, the binary tree decomposition strategy can better handle complex logical structures and improve the overall reasoning performance of the LACT framework.

Given the strong performance of LACT, how could the insights from this work be applied to enhance the logical reasoning capabilities of LLMs in other knowledge-intensive tasks beyond just knowledge graph querying?

The insights from the LACT framework can be applied to enhance the logical reasoning capabilities of Large Language Models (LLMs) in various knowledge-intensive tasks beyond knowledge graph querying by:

Task-specific Fine-tuning: Tailoring the fine-tuning process of LLMs to specific tasks by incorporating task-specific logical reasoning instructions and curriculum learning strategies.
Incorporating Domain Knowledge: Integrating domain-specific knowledge bases and ontologies into the training data to enrich the model's understanding of complex logical relationships in different domains.
Expanding Logical Operators: Extending the range of logical operators and structures that the LLMs can handle, enabling them to perform more sophisticated reasoning tasks across diverse domains.
Transfer Learning: Leveraging the transferability of the LACT framework to adapt LLMs to new tasks and domains, allowing for efficient knowledge transfer and adaptation.

By applying these insights, LLMs can be empowered to excel in a wide range of knowledge-intensive tasks that require complex logical reasoning, contributing to advancements in natural language understanding and reasoning capabilities.