LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities (2305.13168v3)

Abstract: This paper presents an exhaustive quantitative and qualitative evaluation of LLMs for Knowledge Graph (KG) construction and reasoning. We engage in experiments across eight diverse datasets, focusing on four representative tasks encompassing entity and relation extraction, event extraction, link prediction, and question-answering, thereby thoroughly exploring LLMs' performance in the domain of construction and inference. Empirically, our findings suggest that LLMs, represented by GPT-4, are more suited as inference assistants rather than few-shot information extractors. Specifically, while GPT-4 exhibits good performance in tasks related to KG construction, it excels further in reasoning tasks, surpassing fine-tuned models in certain cases. Moreover, our investigation extends to the potential generalization ability of LLMs for information extraction, leading to the proposition of a Virtual Knowledge Extraction task and the development of the corresponding VINE dataset. Based on these empirical findings, we further propose AutoKG, a multi-agent-based approach employing LLMs and external sources for KG construction and reasoning. We anticipate that this research can provide invaluable insights for future undertakings in the field of knowledge graphs. The code and datasets are in https://github.com/zjunlp/AutoKG.

Evaluating LLMs in Knowledge Graph Construction and Reasoning

The paper entitled "LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities" offers a comprehensive evaluation of LLMs, specifically their utility in the process of building and interpreting Knowledge Graphs (KGs). The authors systematically assess the abilities of LLMs, with a primary focus on GPT-4, across diverse datasets. They examine critical tasks including entity and relation extraction, event extraction, link prediction, and question-answering, establishing a robust empirical framework in the domain of construction and inference.

Key Findings

The paper provides quantitative and qualitative assessments, revealing that LLMs function more effectively as inference assistants rather than as few-shot information extractors. For KG construction tasks, while LLMs like GPT-4 perform adequately, they truly excel in reasoning tasks, sometimes outperforming fine-tuned models. This nuanced understanding signals LLMs’ inherent suitability for reasoning-related tasks in KGs, although room for improvement exists in information extraction.

Evaluation Techniques

The paper systematically reviews LLMs using eight diverse datasets, which encapsulate different domains and types. It benchmarks LLM performance against state-of-the-art models using metrics such as F1 scores, Hits@1, and BLEU scores, in both zero-shot and one-shot settings.

1. Entity and Relation Extraction: GPT-4 demonstrates improvement over previous iterations, but it does not match fine-tuned SOTA models. Its performance benefits from example-based instruction in one-shot contexts.
2. Event Extraction: Although GPT-4 often identifies multiple event types correctly, it occasionally struggles with complex sentences, indicating limitations in completely understanding implicit dataset types.
3. Link Prediction: Here, GPT-4 approaches the SOTA performance, showing particular strength with optimized prompts, evidenced in tasks involving the prediction of tail entities.
4. Question Answering: GPT-4 largely matches the SOTA for open-domain QA but struggles on tasks with multiple answers or extensive token constraints.

Generalization vs. Memorization

A significant discussion point is whether the LLMs' performance is driven by memorized training data or genuine generalization from instructions. To investigate this, the authors introduce the Virtual Knowledge Extraction task, supported by the VINE dataset. Results from this innovative task suggest that GPT-4 exhibits strong generalization capabilities, signifying the model's aptitude in understanding and applying new instructions rather than merely recalling memorized facts.

Future Directions: AutoKG

Based on empirical findings, the authors propose AutoKG, a visionary approach to KG construction and reasoning using multi-agent systems. AutoKG leverages LLMs alongside external data sources to foster more autonomous and expansive KG construction processes. This framework incorporates communicative agents that interact with external resources to enhance performance, advancing the collective knowledge graph landscape.

Implications and Future Research

The implications of this research are multifaceted:

• Practical Applications: Enhanced reasoning capacity in LLMs can lead to improved performance in applications like automated QA systems, recommendation systems, and search engines.
• Theoretical Contributions: This work offers a greater understanding of the trade-offs between reasoning and extraction within LLMs, inspiring future investigations into hybrid approaches that combine fine-tuning with generalized learning.

As these insights illuminate potential pathways, continued exploration into data efficiency, interaction design, and prompt engineering will be vital for further advancement in the use of LLMs for knowledge graphs. Future research may also focus on expanding the scope of tasks included under KG-related challenges, such as multimodal reasoning, to leverage the full potential of evolving LLMs.