English(EN)Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem
新的RAG研究解决了表格数据、成本和跨语言知识问题
作者PulseAugur 编辑部·[18 个来源]·
几篇最新的研究论文探讨了检索增强生成（RAG）系统的进展。一篇论文介绍了正交子空间分解（OSD），用于将特定任务行为与参数化RAG中的文档知识分离开来，从而改进适配器组合。另一篇论文CroSearch-R1提出了一个框架，通过将多语言信息整合到强化学习过程中，更好地利用跨语言知识进行RAG。此外，研究还探讨了指代消解对RAG的影响，证明其能够减少歧义并提高性能，特别是对于较小的模型。其他研究则侧重于通过重排分析增强RAG在金融报告等特定领域的应用，以及使用语义缓存进行知识图谱问答。
AI
arXiv:2605.00318v1 Announce Type: new Abstract: Tabular documents such as CSV and Excel files are widely used in enterprise data pipelines, yet existing chunking strategies for retrieval-augmented generation (RAG) are primarily designed for unstructured text and do not account fo…
arXiv cs.LG
TIER_1English(EN)·Shawqi Al-Maliki, Ammar Gharaibeh, Mohamed Rahouti, Mohammad Ruhul Amin, Mohamed Abdallah, Junaid Qadir, Ala Al-Fuqaha·
arXiv:2604.26981v1 Announce Type: cross Abstract: Large Language Models (LLMs) have revolutionized the field of natural language processing. However, they exhibit some limitations, including a lack of reliability and transparency: they may hallucinate and fail to provide sources …
Tabular documents such as CSV and Excel files are widely used in enterprise data pipelines, yet existing chunking strategies for retrieval-augmented generation (RAG) are primarily designed for unstructured text and do not account for tabular structure. We propose a structure-awar…
arXiv:2603.06198v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a framework in which a Generator, such as a Large Language Model (LLM), produces answers by retrieving documents from an external collection using a Retriever. In practice, Generators must…
arXiv cs.CL
TIER_1English(EN)·Yushi Sun, Lei Chen·
arXiv:2604.26176v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) has significantly advanced Knowledge Graph Question Answering (KGQA). However, existing LLM-driven KGQA systems act as stateless planners, g…
arXiv cs.CL
TIER_1English(EN)·Weihang Su, Hanwen Zhang, Qingyao Ai, Yiqun Liu·
arXiv:2604.26768v1 Announce Type: new Abstract: Parametric Retrieval-Augmented Generation (PRAG) encodes external documents into lightweight parameter modules that can be retrieved and merged at inference time, offering a promising alternative to in-context retrieval augmentation…
Parametric Retrieval-Augmented Generation (PRAG) encodes external documents into lightweight parameter modules that can be retrieved and merged at inference time, offering a promising alternative to in-context retrieval augmentation. Despite its potential, many PRAG implementatio…
arXiv:2603.16877v2 Announce Type: replace Abstract: Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a Retrieval-Augmented Generation (RAG) system designed to answer questions about …
arXiv:2604.25182v1 Announce Type: new Abstract: A multilingual collection may contain useful knowledge in other languages to supplement and correct the facts in the original language for Retrieval-Augmented Generation (RAG). However, the vanilla approach that simply concatenates …
arXiv cs.CL
TIER_1English(EN)·Youngjoon Jang, Seongtae Hong, Junyoung Son, Sungjin Park, Chanjun Park, Heuiseok Lim·
arXiv:2507.07847v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and reducing hallucinations by integrating external document retrieval with large langua…
The integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) has significantly advanced Knowledge Graph Question Answering (KGQA). However, existing LLM-driven KGQA systems act as stateless planners, generating retrieval plans in isolation without exp…
arXiv cs.LG
TIER_1English(EN)·Zhuoling Li, Ha Linh Hong Tran Nguyen, Valeria Bladinieres, Maxim Romanovsky·
arXiv:2604.24623v1 Announce Type: cross Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) extends traditional RAG by using knowledge graphs (KGs) to give large language models (LLMs) a structured, semantically coherent context, yielding more grounded answers. Howeve…
arXiv cs.AI
TIER_1English(EN)·Miao Xie, Xiao Zhang, Yi Li, Chunli Lv·
arXiv:2604.22843v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has been proposed to mitigate hallucinations in large language models (LLMs), where generated outputs may be factually incorrect. However, existing RAG approaches predominantly rely on vector s…
arXiv:2604.22757v1 Announce Type: cross Abstract: We introduce StratRAG, an open-source retrieval evaluation dataset for benchmarking Retrieval-Augmented Generation (RAG) systems on multi-hop reasoning tasks under realistic, noisy document-pool conditions. Derived from HotpotQA (…
arXiv:2510.11541v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, which require the identification of multiple know…
A multilingual collection may contain useful knowledge in other languages to supplement and correct the facts in the original language for Retrieval-Augmented Generation (RAG). However, the vanilla approach that simply concatenates multiple pieces of knowledge from different lang…
Graph-based Retrieval-Augmented Generation (GraphRAG) extends traditional RAG by using knowledge graphs (KGs) to give large language models (LLMs) a structured, semantically coherent context, yielding more grounded answers. However, GraphRAG reasoning process remains a black-box,…
arXiv cs.CL
TIER_1English(EN)·Lichang Song, Ting Long, Yi Chang·
arXiv:2602.18734v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has demonstrated strong effectiveness in knowledge-intensive tasks by grounding language generation in external evidence. Despite its success, many existing RAG systems are built based on a r…
