A uto RE : Document-Level Relation Extraction with Large Language Models (2024)

Abstract: Large Language Models (LLMs) have demonstrated exceptional abilities in comprehending and generating text, motivating numerous researchers to utilize them for Information Extraction (IE) purposes, including Relation Extraction (RE). Nonetheless, most existing methods are predominantly designed for Sentence-level Relation Extraction (SentRE) tasks, which typically encompass a restricted set of relations and triplet facts within a single sentence. Furthermore, certain approaches resort to treating relations as candidate choices integrated into prompt templates, leading to inefficient processing and suboptimal performance when tackling Document-Level Relation Extraction (DocRE) tasks, which entail handling multiple relations and triplet facts distributed across a given document, posing distinct challenges. To overcome these limitations, we introduce AutoRE, an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts). Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. Additionally, we have developed an easily extensible RE framework using a Parameters Efficient Fine Tuning (PEFT) algorithm (QLoRA). Our experiments on the RE-DocRED dataset showcase AutoRE’s best performance, achieving state-of-the-art results, surpassing TAG by 10.03% and 9.03% respectively on the dev and test set. The code is available and the demonstration video is provided.

## introduction the rise of llms, such as gpt-4 @xcite and llama2 @xcite , has significantly propelled the progress of natural language processing due to their strong capabilities 1 https://github.com/thudm/autore 2 https://www.youtube.com/watch?@xmath0. ## related work docre refers to the task of extracting relations between entities at the document level, we follow the definition in @xcite : given a document d with a set of sentences containing a set of entities @xmath1. the docre task is to predict the relation types between an entity pair (e h , e t ) h,@xmath2,••• ,n },h̸ =t , where h stands for the head (subject) and t stands for the tail (object). ## conclusion in this paper, we introduce rhf, a new paradigm for re, alongside autore, an advanced docre model. autore represents a cutting-edge approach to the docre task, utilizing llms combined with qlora. this innovative model establishes a new standard, achieving sota results on the re-docred dataset. autore proficiently addresses the intricate task of extracting multiple relations from document-level texts, a significant challenge that has stymied existing models. our future goal is to create a comprehensive, unified framework for re, fully leveraging the capabilities and promise of this paradigm.