End-to-End Trainable Retrieval-Augmented Generation for Relation Extraction
This paper addresses a crucial challenge in retrieval-augmented generation-based relation extractors; the end-to-end training is not applicable to conventional retrieval-augmented generation due to the non-differentiable nature of instance retrieval. This problem prevents the instance retrievers from being optimized for the relation extraction task, and conventionally it must be trained with an objective different from that for relation extraction. To address this issue, we propose a novel End-to-end Trainable Retrieval-Augmented Generation (ETRAG), which allows end-to-end optimization of the entire model, including the retriever, for the relation extraction objective by utilizing a differentiable selection of the $k$ nearest instances. We evaluate the relation extraction performance of ETRAG on the TACRED dataset, which is a standard benchmark for relation extraction. ETRAG demonstrates consistent improvements against the baseline model as retrieved instances are added. Furthermore, the analysis of instances retrieved by the end-to-end trained retriever confirms that the retrieved instances contain common relation labels or entities with the query and are specialized for the target task. Our findings provide a promising foundation for future research on retrieval-augmented generation and the broader applications of text generation in Natural Language Processing.
Code (0)
등록된 구현이 없습니다.
Tasks
RelationRelation ExtractionRetrievalRetrieval-augmented GenerationText GenerationSimilar Papers 제목 키워드 기반
GOSU: Retrieval-Augmented Generation with Global-Level Optimized Semantic Unit-Centric Framework
Building upon the standard graph-based Retrieval-Augmented Generation (RAG), the introduction of heterogeneous graphs and hypergraphs aims to enrich retrieval and generation by leveraging the relationships between multip…
Coreference ResolutionKeyword ExtractionLinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora
Retrieval-Augmented Generation (RAG) is widely used to mitigate hallucinations of Large Language Models (LLMs) by leveraging external knowledge. While effective for simple queries, traditional RAG systems struggle with l…
Relation ExtractionPassage RetrievalKnowledge GraphsExposing Privacy Risks in Graph Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is a powerful technique for enhancing Large Language Models (LLMs) with external, up-to-date knowledge. Graph RAG has emerged as an advanced paradigm that leverages graph-based knowle…
Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs
Event temporal relation (TempRel) is a primary subject of the event relation extraction task. However, the inherent ambiguity of TempRel increases the difficulty of the task. With the rise of prompt engineering, it is im…
Event Relation ExtractionPrompt EngineeringRelationRelation Extraction+2RACap: Relation-Aware Prompting for Lightweight Retrieval-Augmented Image Captioning
Recent retrieval-augmented image captioning methods incorporate external knowledge to compensate for the limitations in comprehending complex scenes. However, current approaches face challenges in relation modeling: (1) …
Image Captioning