Learning Representations for Reasoning: Generalizing Across Diverse Structures (2410.13018v1)

Abstract: Reasoning, the ability to logically draw conclusions from existing knowledge, is a haLLMark of human. Together with perception, they constitute the two major themes of artificial intelligence. While deep learning has pushed the limit of perception beyond human-level performance, the progress in reasoning domains is way behind. One fundamental reason is that reasoning problems usually have flexible structures for both knowledge and queries, and many existing models only perform well on structures seen during training. Here we aim to push the boundary of reasoning models by devising algorithms that generalize across knowledge and query structures, as well as systems that accelerate development on structured data. This thesis consists of three parts. In Part I, we study models that can inductively generalize to unseen knowledge graphs with new entity and relation vocabularies. For new entities, we propose a framework that learns neural operators in a dynamic programming algorithm computing path representations. For relations, we construct a relation graph to capture the interactions between relations, thereby converting new relations into new entities. In Part II, we propose two solutions for generalizing across multi-step queries on knowledge graphs and text respectively. For knowledge graphs, we show that multi-step queries can be solved by multiple calls of graph neural networks and fuzzy logic operations. For text, we devise an algorithm to learn explicit knowledge as textual rules to improve LLMs on multi-step queries. In Part III, we propose two systems to facilitate machine learning development on structured data. Our library treats structured data as first-class citizens and removes the barrier for developing algorithms on structured data. Our node embedding system solves the GPU memory bottleneck of embedding matrices and scales to graphs with billion nodes.

• The paper introduces NBFNet, a novel graph neural network that computes path representations in knowledge graphs, achieving notable gains in HITS@1 and HITS@10 metrics.
• It presents A*Net, an advanced framework that leverages priority functions to optimize search processes, reducing computation while preserving accuracy.
• The work demonstrates zero-shot generalization and enhanced multi-hop query execution through Ultra, GNN-QE, and Hypotheses-to-Theories, paving the way for unified reasoning models.

Analyzing "Learning Representations for Reasoning: Generalizing Across Diverse Structures"

The dissertation by Zhaocheng Zhu "Learning Representations for Reasoning: Generalizing Across Diverse Structures" presents significant strides in the domain of representation learning, focusing on generalization across varying structures in reasoning tasks. The work is structured into interconnected parts that explore models intending to generalize knowledge structures and query systems that advance reasoning capabilities.

Key Contributions:

1. Generalization Across Knowledge Structures: The work introduces the Neural BeLLMan-Ford Networks (NBFNet), a novel GNN framework that extends the BeLLMan-Ford algorithm. This model allows for the computation of path representations within knowledge graphs, effectively managing inductive generalization by predicting entity representations as functions of relations. Empirical results show that NBFNet outperforms traditional embedding methods with significant gains in performance metrics, such as HITS@1 and HITS@10.
2. Scalability with A*Net: Further enhancing scalability, the A*Net represents an evolution of NBFNet by incorporating priority functions reminiscent of the A* algorithm. This model optimizes path search processes on large-scale knowledge graphs using neural gradient-based approaches, demonstrating reduced computational needs without sacrificing accuracy.
3. Zero-shot Generalization with Ultra: The Ultra model addresses limitations in handling diverse relation vocabularies by employing relative relation representations. It effectively captures relational interactions within a graph, paving the way for zero-shot generalizations across previously unseen datasets. Ultra has shown robust performance across multiple graphs differing in domains and sizes, marking a substantial leap towards a unified reasoning framework.
4. Handling Multi-hop Queries: Through Graph Neural Network Query Executor (GNN-QE), Zhu addresses the challenge of multi-step queries, enabling models to deconstruct such queries into logical and projection components. This approach facilitates improved handling of complex queries through an inductive setting, which significantly enhances interpretability by aligning neural operations closer to symbolic reasoning methods.
5. Hypotheses-to-Theories for LLMs: In conjunction with LLMs, the Hypotheses-to-Theories (HtT) methodology introduces a paradigm where LLMs learn explicit textual rules, mitigating deficiencies in implicit knowledge inherent in these models. Applied across relational, numerical, and concept learning tasks, HtT improves reasoning robustness, particularly for counterfactual settings, without exhaustive exemplars.

Implications and Future Directions:

The implications of Zhu's work are profound, setting a foundation for scalable, universally applicable reasoning models. The methodologies presented lay groundwork for future research that can explore:

• Enhanced integration of neural-symbolic models for reasoning.
• Further scalability evaluation in real-world vast databases.
• Exploration of more complex query types and structures, potentially applying models to varied and dynamic datasets.

In summary, Zhu's dissertation considerably advances the understanding and abilities of reasoning models, demonstrating noteworthy performance improvements across a range of reasoning tasks and conditions. The blend of novel techniques and attention to scalable ML systems suggests a convergence towards more adaptive, expansive frameworks in AI reasoning.