Open-Domain QA

QA: Question Answering

Follow this topic Ask about this topic

Momentum

2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 10

All topics

Bridging Semantic Gaps in RAG through Generated Context Knowledge Fusion
Sep 29, 2026Xinkai Du, Chao Lv, Yalin Sun +3Retrieval-Augmented Generation Semantic Search
Beijing Wanlian Zhilian Technology Corporation Limited, Beijing, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China · Sunshine Digital Intelligence Tech Co., Ltd., Beijing, China
Informative Viewpoint Selection for Episodic-Memory Embodied Question Answering using Omnidirectional Images
Sep 27, 2026Kaname Kitamura, Asako KanezakiEmbodied QA Open-Domain QA
Department of Computer Science, Institute of Science Tokyo, Tokyo, Japan · RIKEN, Japan · Tohoku University, Sendai, Japan
Compression Beyond the Uncompressed: A Two-Stage Training Recipe for Soft Context Compression in RAG
Sep 4, 2026Shuyu Guo, Shuo Zhang, Zhaochun RenRetrieval-Augmented Generation LLM Inference Acceleration
Shandong University Qingdao, China · Bloomberg London, United Kingdom · Leiden University Leiden, The Netherlands
Diversity is Not Ambiguity: Toward Accurate and Efficient Ambiguity Detection for Open-Domain QA
Aug 4, 2026Jiwon Lee, Yong-chan Park, Jungin Hong +1Question Answering Open-Domain QA
Seoul National University
EviBack: Search-Agent Reinforcement Learning via Evidence-Constrained Teacher Backoff
Jul 27, 2026Xiao Ma, Zhiquan Hu, Yi Wei +5Reinforcement Learning Agentic RAG
NEXTAI Research Institute, Chery Group
BLUEX v2: Benchmarking LLMs on Open-Ended Questions from Brazilian University Entrance Exams
Jun 21, 2026João Guilherme Alves Santos, Giovana Kerche Bonás, Thiago Laitz +2Multilingual Language Model Evaluation LLM Evaluation
University of Campinas (UNICAMP), Campinas-SP, Brazil · Tropic AI, Campinas-SP, Brazil · Maritaca AI, Campinas-SP, Brazil
CAPF: Guiding Search-Agent Rollouts with Credit-Attenuated Privileged Feedback
Jun 1, 2026Bin Chen, Xinye Liao, Yiming Liu +2RL for Language Model Reasoning Credit Assignment in RL
SCOPE: Self-Play via Co-Evolving Policies for Open-Ended Tasks
May 29, 2026Wai-Chung Kwan, Aryo Pradipta Gema, Joshua Ong Jun Leang +1Open-Ended Generation Self-Play RL
University of Edinburgh · Imperial College London
EAPO: Entropy-Driven Adaptive Positive-Negative Sample Weighting for Policy Optimization in Open-Ended QA
May 27, 2026Yunsheng Zeng, Gen Li, Yuwei Miao +8RL for Language Models Reinforcement Learning with Verifiable Rewards
1Baidu Inc.
MiRD: Reliable Set-Valued Prediction for Open-Ended Question Answering via Miscoverage Risk Decomposition
May 25, 2026Anqi Hu, Zhiyuan Wang, Zijun Jia +1LLM Hallucination Mitigation Set Prediction
University of Electronic Science and Technology of China · Beihang University

Sep 29, 2026, cs.CLJ/K move · Enter open · S save

Bridging Semantic Gaps in RAG through Generated Context Knowledge Fusion

Xinkai Du, Chao Lv, Yalin Sun, Quanjie Han+2

Beijing Wanlian Zhilian Technology Corporation Limited, Beijing, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China · Sunshine Digital Intelligence Tech Co., Ltd., Beijing, China

Retrieval-Augmented Generation Semantic Search Open-Domain QA

Open paper AskPDF arXiv

Retrieval-Augmented Generation has established itself as a fundamental framework in natural language processing, seamlessly integrating information retrieval with the generative capabilities of large language models. However, this process is fundamentally constrained by a critical challenge: semantic space mismatch between queries and retrieved contexts. We propose Knowledge-Aware Semantic Bridging (KASB), a novel framework that improves passage selection quality through semantic space alignment between queries and retrieved documents through intelligent knowledge fusion. Our approach leverages the complementary strengths of generative and retrieval-based knowledge through a multistage process that enhances both relevance and accuracy. We evaluate KASB on three popular open-domain Question Answering datasets to demonstrate the effectiveness of our approach.

Momentum

Related topics

Latest papers 10