AutoRE: Document-Level Relation Extraction with Large Language Models

Core Concepts

Introducing AutoRE, an innovative end-to-end DocRE model that outperforms existing methods by adopting the RHF paradigm for efficient and accurate extraction of multiple relations and triplet facts from documents.

Abstract

Introduction: Large Language Models (LLMs) have revolutionized text comprehension and generation. Interest in using LLMs for Information Extraction (IE) tasks like Relation Extraction (RE) has grown. Underperformance in DocRE Tasks: High-performing LLMs struggle with Document-Level Relation Extraction (DocRE) tasks. Existing models show suboptimal performance on DocRE tasks involving multiple relations. Limitations of Current RE Paradigms: Modern generative methods fall short in handling DocRE tasks with numerous relations and triplet facts. The RHF paradigm is introduced to address these limitations effectively. Key Contributions: AutoRE excels in DocRE by prioritizing relation extraction followed by subject identification for accurate triplet fact extraction. Utilizes a Parameters Efficient Fine Tuning algorithm (QLoRA) for state-of-the-art results on the Re-DocRED dataset.

Stats

Our experiments on the RE-DocRED dataset showcase AutoRE’s best performance, surpassing TAG by 10.03% and 9.03% respectively on the dev and test set.

Quotes

"LLMs have demonstrated exceptional abilities in comprehending and generating text." "AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios."

Key Insights Distilled From

AutoRE

by Xue Lilong,Z... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14888.pdf

Deeper Inquiries

How can AutoRE's approach be applied to other NLP tasks beyond relation extraction?

AutoRE's approach of utilizing Large Language Models (LLMs) combined with Parameters Efficient Fine Tuning (PEFT) algorithms like QLoRA can be extended to various Natural Language Processing (NLP) tasks. For instance, in Named Entity Recognition (NER), the model could be fine-tuned to extract specific entities from text efficiently. In Event Extraction (EE), it could help identify and classify events mentioned in documents accurately. Additionally, for Sentiment Analysis, AutoRE could assist in determining the sentiment expressed in texts by analyzing relationships between entities or events.

What are potential drawbacks or criticisms of the RHF paradigm compared to traditional RE methods?

One drawback of the Relation-Head-Facts (RHF) paradigm compared to traditional Relation Extraction (RE) methods is its increased complexity and computational demands due to decomposing the task into multiple subtasks. This may lead to longer training times and higher resource requirements. Additionally, RHF may struggle with cases where relations are ambiguous or context-dependent, as it relies on a step-by-step approach that might not capture nuanced relationships effectively. Moreover, ensuring accurate identification of head entities for each relation can pose challenges in scenarios with overlapping information.

How might incorporating external knowledge sources improve AutoRE's performance further?

Incorporating external knowledge sources such as domain-specific databases or ontologies can enhance AutoRE's performance by providing additional context and background information for relation extraction tasks. By leveraging external knowledge bases like Wikidata or domain-specific repositories, AutoRE can validate extracted triplet facts against known data points, improving accuracy and reducing false positives. Furthermore, integrating semantic embeddings from external resources can help enrich entity representations and facilitate better understanding of complex relationships within documents.