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    <abstract>Retrieval-Augmented Generation (RAG) has established itself as an innovative technology enabling Large Language Models (LLMs) to answer difficult questions beyond their internal knowledge. Graph-based RAG, which utilizes a triplet-based graph as a database, offers the advantage of structuring relationships between entities through graph connectivity, thereby capturing connections within the corpus that traditional RAG cannot express. However, this paper points out that the process of converting a corpus into a triplet-based graph using LLMs may be unsuitable for RAG due to the inability to represent hierarchical information and structural rigidity. To address this, we propose a novel database structure called the TRA(Title-Relations-Attributes) structure. Through a series of experiments, we highlight the benefits of indexing TRA Structure as a fact to utilize it as a retrieval-optimized data structure, and demonstrate its powerful multi-hop reasoning performance when combined with query decomposition, while also covering key insights in multi-hop reasoning.</abstract>
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%0 Conference Proceedings
%T Retrieval-Augmented Generation with Retrieval-optimized TRA Structure
%A Kim, JungHyoun
%A Kim, Soohyeong
%A Hwang, Seok Jun
%A Park, Jeonghyeon
%A Choi, Yong Suk
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kim-etal-2026-retrieval-augmented
%X Retrieval-Augmented Generation (RAG) has established itself as an innovative technology enabling Large Language Models (LLMs) to answer difficult questions beyond their internal knowledge. Graph-based RAG, which utilizes a triplet-based graph as a database, offers the advantage of structuring relationships between entities through graph connectivity, thereby capturing connections within the corpus that traditional RAG cannot express. However, this paper points out that the process of converting a corpus into a triplet-based graph using LLMs may be unsuitable for RAG due to the inability to represent hierarchical information and structural rigidity. To address this, we propose a novel database structure called the TRA(Title-Relations-Attributes) structure. Through a series of experiments, we highlight the benefits of indexing TRA Structure as a fact to utilize it as a retrieval-optimized data structure, and demonstrate its powerful multi-hop reasoning performance when combined with query decomposition, while also covering key insights in multi-hop reasoning.
%U https://aclanthology.org/2026.emnlp-main.118/
%P 2735-2762

Markdown (Informal)

[Retrieval-Augmented Generation with Retrieval-optimized TRA Structure](https://aclanthology.org/2026.emnlp-main.118/) (Kim et al., EMNLP 2026)

• Retrieval-Augmented Generation with Retrieval-optimized TRA Structure (Kim et al., EMNLP 2026)

ACL

• JungHyoun Kim, Soohyeong Kim, Seok Jun Hwang, Jeonghyeon Park, and Yong Suk Choi. 2026. Retrieval-Augmented Generation with Retrieval-optimized TRA Structure. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 2735–2762, Budapest, Hungary. Association for Computational Linguistics.