Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion

Core Concepts

A unified framework, TaxoInstruct, effectively addresses Entity Set Expansion, Taxonomy Expansion, and Seed-Guided Taxonomy Construction tasks through taxonomy-guided instruction tuning.

Abstract

The content introduces TaxoInstruct, a unified framework for entity enrichment tasks. It identifies common skills needed for these tasks and proposes a taxonomy-guided instruction tuning approach. Extensive experiments show the effectiveness of TaxoInstruct over task-specific baselines.

Stats

Extensive experiments demonstrate the effectiveness of TaxoInstruct. TaxoInstruct significantly outperforms task-specific baselines across all three tasks.

Quotes

Key Insights Distilled From

A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion

by Yanzhen Shen... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2402.13405.pdf

Deeper Inquiries

How does the generative nature of large language models impact the performance of TaxoInstruct

The generative nature of large language models plays a crucial role in the performance of TaxoInstruct. By leveraging the generative capabilities of these models, TaxoInstruct can effectively extract and exploit self-supervision data from existing taxonomies. This allows the model to learn structural information, such as hypernymy and sibling relations among entities, which is essential for tasks like Entity Set Expansion, Taxonomy Expansion, and Seed-Guided Taxonomy Construction. The ability of large language models to generate new entities based on instructions helps in expanding entity sets and enriching taxonomies with accurate and relevant information.

What are the limitations of using a corpus-independent approach in entity enrichment tasks

Using a corpus-independent approach in entity enrichment tasks has its limitations. While this approach allows TaxoInstruct to generate new entities without relying on domain-specific text data, it may not capture domain knowledge present in textual corpora. In specific domains where textual data contains valuable information related to entities or taxonomy structures, a corpus-independent approach might miss out on important context that could enhance the quality of expanded entities or taxonomy construction. Additionally, without utilizing domain-specific text data, there is a risk of missing out on nuanced relationships between entities that are only evident in specialized texts.

How does TaxoInstruct perform on a directed acyclic graph (DAG) taxonomy with multiple parents for nodes

When applied to a directed acyclic graph (DAG) taxonomy with multiple parents for nodes, TaxoInstruct's performance may be impacted due to certain challenges unique to this type of structure. In a DAG taxonomy where nodes can have multiple parents, determining the correct parent for each node becomes more complex compared to a simple tree structure where each node has only one parent. The model would need additional mechanisms or modifications to handle cases where an entity can belong under different parent nodes based on different criteria or contexts within the taxonomy hierarchy. Ensuring accurate placement within such complex taxonomies would require careful consideration of how multiple parent relationships are managed during expansion processes.