- The paper introduces the Chain-of-Layer framework that iteratively prompts LLMs to generate robust taxonomies using an Ensemble-based Ranking Filter.
- The methodology leverages hierarchical instructions, few-shot demonstrations, and iterative inference to significantly improve precision and recall.
- Experimental results across four benchmarks demonstrate the framework's scalability and effectiveness in both few-shot and zero-shot settings.
Iterative Framework for Enhancing Taxonomy Induction from LLMs
Introduction
Taxonomy induction has remained a focal point of interest due to its critical role in structuring knowledge for web search, recommendation systems, and question-answering applications. Traditional approaches have largely depended on discriminative and generative methods, each with its limitations. This paper introduces the Chain-of-Layer (CoL) framework, an innovative approach designed to iteratively prompt LLMs for taxonomy induction from a given set of entities. Central to CoL is the Ensemble-based Ranking Filter, aimed at minimizing errors and reducing the hallucinated content in the generated taxonomy.
Problem Definition
Taxonomies, representing hierarchical relationships between entities, are fundamental in organizing knowledge. The objective of taxonomy induction is to construct a directed acyclic graph, where the vertices represent conceptual entities, and the edges define the parent-child "is-a" relationships. Manual curation of taxonomies is labor-intensive and not scalable, hence the shift towards automatic taxonomy construction methods.
Methodology - Chain-of-Layer Framework
The CoL framework is articulated around breaking down the taxonomy induction task iteratively, focusing on layer-to-layer generation and refinement. The process entails:
- Hierarchical Format Taxonomy Induction Instruction (HF): A novel instruction format that leverages the hierarchical structure of entities to improve the inducted taxonomy's quality.
- Few-shot Demonstration Construction: Utilizing demonstrations for CoL inference, aiming to simulate the process of incremental taxonomy induction.
- Iterative Inference via CoL: Detailed descriptions of iterative inference with CoL and the incorporation of the Ensemble-based Ranking Filter.
- Extension to Zero-shot Setting (CoL-Zero): Adapting CoL to domains lacking well-inducted taxonomies by leveraging LLMs to generate demonstrations.
Experiments and Evaluation
The efficacy of CoL is demonstrated through extensive experiments across four real-world benchmarks. The framework's performance was evaluated against both supervised fine-tuning and unsupervised baseline methods, showcasing significant improvements in the precision and recall metrics of taxonomy induction tasks. In particular, CoL achieves remarkable performance in both few-shot and zero-shot settings, underscoring its scalability and domain generalization capabilities.
Ablation Study
An ablation paper further elucidates the contributions of the CoL framework's core components: the iterative prompting mechanism and the Ensemble-based Ranking Filter. Results confirm that both components are critical in enhancing the performance of taxonomy induction tasks, significantly reducing error propagation and improving the overall quality of the generated taxonomy.
Conclusion
This paper presents Chain-of-Layer (CoL), a robust framework for taxonomy induction that innovatively leverages the capabilities of LLMs. CoL's iterative approach, grounded in structured instructions and augmented by an Ensemble-based Ranking Filter, sets a new benchmark in automatic taxonomy construction. Addressing the limitations of previous methods, CoL exhibits superior performance in constructing coherent and accurate taxonomies. Looking ahead, the framework opens new avenues for exploring taxonomy induction in varied domains and further refining the integration of LLMs in knowledge structuring tasks.
Future Directions
The findings pose intriguing questions for future research, particularly in exploring the adaptation of CoL to broader domains and further refining the Ensemble-based Ranking Filter. Additionally, investigating the scalability of CoL and its effectiveness in even larger taxonomy induction tasks presents an exciting challenge for future work in the field of AI and knowledge management.