Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking

2024-12-31

AI Summary

Structured Knowledge Prompting (SKP) methods lack systematic evaluation of generalization capabilities across varying task difficulty, knowledge granularity, and domain shifts. Method: We propose a multidimensional generalizability analysis framework—assessing granularity sensitivity, cross-task transferability, scale scalability, and domain universality—and introduce SUBARU, the first benchmark supporting multi-granularity, multi-level evaluation across nine diverse tasks. We further design a standardized evaluation pipeline integrating structured knowledge injection, fine-grained modeling, and transfer validation. Results: Empirical analysis reveals severe generalization limitations in current SKP approaches: performance degrades significantly with finer-grained knowledge specifications and cross-task adaptation. Our findings provide rigorous empirical evidence for robust knowledge injection and identify critical directions for improvement—particularly in granularity-aware prompting, transfer-efficient architectures, and domain-adaptive knowledge grounding.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud hanging over the LLM skyscraper. Structural knowledge prompting (SKP) is a prominent paradigm to integrate external knowledge into LLMs by incorporating structural representations, achieving state-of-the-art results in many knowledge-intensive tasks. However, existing methods often focus on specific problems, lacking a comprehensive exploration of the generalization and capability boundaries of SKP. This paper aims to evaluate and rethink the generalization capability of the SKP paradigm from four perspectives including Granularity, Transferability, Scalability, and Universality. To provide a thorough evaluation, we introduce a novel multi-granular, multi-level benchmark called SUBARU, consisting of 9 different tasks with varying levels of granularity and difficulty.

Problem

• Structural Knowledge Guidance
• Large Language Models
• Task Performance

Innovation

• SUBARU Testing Framework
• Structural Knowledge Guidance
• Large Language Models