arXiv Computation and Language September 30, 2026 By Xinkai Du, Chao Lv, Yalin Sun, Quanjie Han, Lei Yao, Maosong Sun

Bridging Semantic Gaps in RAG through Generated Context Knowledge Fusion

Read the original on arXiv Computation and Language →

The paper introduces Knowledge-Aware Semantic Bridging (KASB), a framework designed to improve Retrieval-Augmented Generation (RAG) by aligning the semantic spaces of queries and retrieved contexts. KASB achieves this through intelligent fusion of generative and retrieval-based knowledge in a multistage process, aiming to enhance passage selection quality, relevance, and accuracy. The authors evaluate the method on three popular open-domain Question Answering datasets, demonstrating its effectiveness.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv AI

Jul 21

A Survey on Knowledge-Oriented Retrieval-Augmented Generation

arXiv:2503. 10677v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has gained significant attention in recent years for its potential to enhance natural language understanding and generation by combining large-scale retrieval systems with generative models.

By Mingyue Cheng, Yucong Luo, Jie Ouyang, Qi Liu, Huijie Liu, Li Li, Shuo Yu, Bohou Zhang, Jiawei Cao, Jie Ma, Daoyu Wang, Enhong Chen

rag nlp multimodal benchmarks safety

arXiv AI

Jul 14

HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA

arXiv:2402. 01767v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has rapidly advanced the language model field, particularly in question-answering (QA) systems.

By Xinyue Chen, Pengyu Gao, Jiangjiang Song, Xiaoyang Tan

llms rag benchmarks safety

arXiv AI

Jun 12

Uncertainty-Aware Hybrid Retrieval for Long-Document RAG

arXiv:2606. 13550v1 Announce Type: new Abstract: Retrieval augmented generation (RAG) depends critically on the quality and granularity of retrieved evidence.

By Hoin Jung, Xiaoqian Wang

rag nlp benchmarks

arXiv Machine Learning

4d ago

UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

arXiv:2603.28773v2 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon of...

By Dobrik Georgiev, Kheeran K. Naidu, Alberto Cattaneo, Federico Monti, Carlo Luschi, Daniel Justus

llms rag nlp benchmarks safety

arXiv AI

Sep 10

Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrieval

arXiv:2609.07050v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) critically depends on retrieving the evidence necessary for effective reasoning. However, this remains particularl...

By JungMin Yun, YoungBin Kim

rag nlp

arXiv Computation and Language

Sep 14

EAR: Entity-Aware Partitioning Approach for Retrieval-Augmented Generation Development

The paper introduces EAR, an Entity‑Aware Partitioning approach that improves retrieval‑augmented generation for multiple‑choice question answering by extracting normalized surface anchors from questions, answers, and the corpus. EAR retrieves local windows around matching anchors and can attach a larger parent passage via an extractive summary, reducing retrieved words by 37.5‑40.2% compared to fixed‑size chunks. Experiments on a cleaned MMLU‑style subset with Mistral, Gemma, and DeepSeek show modest accuracy changes, none statistically significant, highlighting EAR’s methodological contribution of compact, inspectable retrieval units.

By Cenab Batu Bora, Oylum Alatl{\i}, Sebnem Bora, Oguz Dikenelli

llms rag nlp benchmarks

One email a morning, machine-written

Related stories

A Survey on Knowledge-Oriented Retrieval-Augmented Generation

HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA

Uncertainty-Aware Hybrid Retrieval for Long-Document RAG

UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrieval

EAR: Entity-Aware Partitioning Approach for Retrieval-Augmented Generation Development