RAG / 检索增强生成

Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives

Si Sun, Chenyan Xiong, Yue Yu, Arnold Overwijk, Zhiyuan Liu, Jie Bao

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.CL

Comments Accepted to EMNLP 2022 main conference

Adversarial Retriever-Ranker for dense text retrieval

Hang Zhang, Yeyun Gong, Yelong Shen, Jiancheng Lv, Nan Duan, Weizhu Chen

专题命中 检索器与排序 ：retriever(title,abstract)；分类 cs.CL

Comments ICLR 2022

Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning

Xingwei He, Yeyun Gong, A-Long Jin, Weizhen Qi, Hang Zhang, Jian Jiao, Bartuer Zhou, Biao Cheng, SM Yiu, Nan Duan

专题命中 检索器与排序 ：retriever(title,abstract)；分类 cs.CL

Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval

Wei Zhong, Jheng-Hong Yang, Yuqing Xie, Jimmy Lin

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR

Saving Dense Retriever from Shortcut Dependency in Conversational Search

Sungdong Kim, Gangwoo Kim

专题命中 检索器与排序 ：retriever(title,abstract)；分类 cs.CL

Comments Accepted to EMNLP 2022 main conference

A Contrastive Pre-training Approach to Learn Discriminative Autoencoder for Dense Retrieval

Xinyu Ma, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Xueqi Cheng

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR

Comments Accepted by CIKM2022

On the Value of Behavioral Representations for Dense Retrieval

Nan Jiang, Dhivya Eswaran, Choon Hui Teo, Yexiang Xue, Yesh Dattatreya, Sujay Sanghavi, Vishy Vishwanathan

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR

Distilling Knowledge from Reader to Retriever for Question Answering

Gautier Izacard, Edouard Grave

专题命中 检索器与排序 ：retriever(title,abstract)；分类 cs.CL

A Numerical Reasoning Question Answering System with Fine-grained Retriever and the Ensemble of Multiple Generators for FinQA

Bin Wang, Jiangzhou Ju, Yunlin Mao, Xin-Yu Dai, Shujian Huang, Jiajun Chen

专题命中 检索器与排序 ：retriever(title,abstract)；分类 cs.CL

Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings

Georgios Sidiropoulos, Evangelos Kanoulas

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR

Comments Accepted at the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022)

Curriculum Learning for Dense Retrieval Distillation

Hansi Zeng, Hamed Zamani, Vishwa Vinay

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR

Comments Accepted to SIGIR 2022

Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction

Xinyu Ma, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Xueqi Cheng

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR

Comments Accepted to SIGIR 2022

Auto-MLM: Improved Contrastive Learning for Self-supervised Multi-lingual Knowledge Retrieval

Wenshen Xu, Mieradilijiang Maimaiti, Yuanhang Zheng, Xin Tang, Ji Zhang

专题命中 检索器与排序 ：knowledge retrieval(title,abstract)；分类 cs.CL

Comments 9 pages, 5 figures, 3 tables

DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies

Pok Wah Chan

专题命中 检索器与排序 ：knowledge retrieval(title,abstract)；分类 cs.CL

Comments 72 pages

Interpreting Dense Retrieval as Mixture of Topics

Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR

Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering

Man Luo, Yankai Zeng, Pratyay Banerjee, Chitta Baral

专题命中 检索器与排序 ：retriever(title,abstract)；分类 cs.CL

Comments accepted at EMNLP 2021

DoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval

Sophia Althammer, Arian Askari, Suzan Verberne, Allan Hanbury

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR

Comments Published in COLIEE 2021

More Robust Dense Retrieval with Contrastive Dual Learning

Yizhi Li, Zhenghao Liu, Chenyan Xiong, Zhiyuan Liu

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR

Comments Accepted by ICTIR 2021

Weakly Supervised Pre-Training for Multi-Hop Retriever

Yeon Seonwoo, Sang-Woo Lee, Ji-Hoon Kim, Jung-Woo Ha, Alice Oh

专题命中 检索器与排序 ：retriever(title,abstract)；分类 cs.CL

Comments ACL-Findings 2021

News Meets Microblog: Hashtag Annotation via Retriever-Generator

Xiuwen Zheng, Dheeraj Mekala, Amarnath Gupta, Jingbo Shang

专题命中 检索器与排序 ：retriever(title,abstract)；分类 cs.CL

Optimizing Dense Retrieval Model Training with Hard Negatives

Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR

Comments To be published in SIGIR2021

Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval

Chen Zhao, Chenyan Xiong, Jordan Boyd-Graber, Hal Daumé

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.CL

Comments NAACL 2021

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

Wenhan Xiong, Xiang Lorraine Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Wen-tau Yih, Sebastian Riedel, Douwe Kiela, Barlas Oğuz

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.CL

X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models

Zhengbao Jiang, Antonios Anastasopoulos, Jun Araki, Haibo Ding, Graham Neubig

专题命中 检索器与排序 ：knowledge retrieval(title,abstract)；分类 cs.CL

Comments EMNLP 2020

Is Retriever Merely an Approximator of Reader?

Sohee Yang, Minjoon Seo

专题命中 检索器与排序 ：retriever(title,abstract)；分类 cs.CL

Learning To Retrieve: How to Train a Dense Retrieval Model Effectively and Efficiently

Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma

专题命中 检索器与排序 ：dense retrieval(title,abstract)；分类 cs.IR

Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152

在块内思考：使用大语言模型生成符合法规的场景的RegulaRAG——以联合国第152号法规为例

Vahid Zolfaghari, Nenad Petrovic, AndrÉ Schamschurko, Alois Knoll

机构 * Technical University of Munich（慕尼黑工业大学） 纠错

专题命中 检索器与排序 ：RAG(abstract,abstract_cn)；retrieval-augmented generation(abstract)；分类 cs.IR、cs.AI

AI总结 针对LLMs难以结合冗长分层标准的问题，提出RegulaRAG流水线，经实验其在UN R152数据集上元分数最高且鲁棒性强，优于基线系统。

Multimodal Language Models Benchmarked Against the NRC Reactor Operator Licensing Examination: Fine-Tuning and Retrieval Strategies

基于美国核管理委员会反应堆操作员执照考试的多模态语言模型微调与检索策略基准测试

Isak Hwang, Yoon Pyo Lee, Syed Bahauddin Alam

机构 * organization= Department of Nuclear Engineering, Hanyang University , addressline= 222 Wangsimni-ro , postcode= 04763 , state= Seongdong-gu , city= Seoul , country= South Korea ； organization= The Grainger College of Engineering, Nuclear, Plasma \& Radiological Engineering, University of Illinois Urbana-Champaign , city= Urbana , state= IL , country= USA 纠错

专题命中 检索器与排序 ：RAG(summary_cn,abstract_cn)；分类 cs.CL、cs.AI

AI总结 该研究针对美国核管理委员会反应堆操作员执照考试，评估310亿参数多模态模型应用核知识的能力，通过对比基础模型与多种微调及检索配置，发现固定大小分块RAG的SFT配置表现最佳，并揭示了分块策略规律及RAFT与SFT的性能差异。

Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations

评估基于大语言模型的目标提取在需求工程中的应用：提示策略及其局限性

Anna Arnaudo, Riccardo Coppola, Maurizio Morisio, Flavio Giobergia, Andrea Bioddo, Angelo Bongiorno, Luca Dadone

机构 * Department of Control and Computer Engineering（控制与计算机工程系） 纠错

专题命中 检索器与排序 ：RAG(abstract,abstract_cn)；retrieval-augmented generation(abstract)；分类 cs.CL、cs.AI

AI总结 本文探讨了通过三个阶段自动提取功能目标以实现目标导向的需求工程，提出基于工程提示的LLM链，实验表明反馈循环机制在零样本学习中表现更优，但提示策略仍是性能限制因素。

Comments 11 pages, 1 figure. This contribution will be published in the conference proceedings of EASE 2026 Conference (https://conf.researchr.org/home/ease-2026/prompt-se-2026)

Risk-Constrained Freshness-Aware Semantic Caching for Open-Web Retrieval-Augmented LLMs

面向开放网络检索增强语言模型的风险约束新鲜度感知语义缓存

Muhammad Mansoor, Tahir Ahmad, Yeo-Chan Yoon

机构 * Jeju National University（济州国立大学） 纠错

专题命中 检索器与排序 ：RAG(abstract,abstract_cn)；retrieval-augmented generation(abstract)；分类 cs.CL、cs.AI

AI总结 研究针对开放网络证据时变新鲜度，提出三层语义缓存FreshCache，将缓存重用视为风险约束时间推理问题，给出评估方法并引入基准测试，实验表明其在节省搜索API及降低陈旧错误率方面效果良好。