RAG / 检索增强生成

Reproducing HotFlip for Corpus Poisoning Attacks in Dense Retrieval

Yongkang Li, Panagiotis Eustratiadis, Evangelos Kanoulas

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

Comments This paper has been accepted for oral presentation in the reproducibility track at ECIR 2025

CacheFocus: Dynamic Cache Re-Positioning for Efficient Retrieval-Augmented Generation

Kun-Hui Lee, Eunhwan Park, Donghoon Han, Seung-Hoon Na

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

Comments 11 pages (Work in progress)

AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment

Yilong Lai, Jialong Wu, Congzhi Zhang, Haowen Sun, Deyu Zhou

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

Comments Accepted by COLING 2025

Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval

João Coelho, Bruno Martins, João Magalhães, Jamie Callan, Chenyan Xiong

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

Advancing Academic Knowledge Retrieval via LLM-enhanced Representation Similarity Fusion

Wei Dai, Peng Fu, Chunjing Gan

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

Comments The 2nd Place of KDD Cup 2024 OAG-Challenge AQA

A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation

Yilong Zhao, Daifeng Li

专题命中 检索器与排序 ：retrieval-augmented generation(title)；分类 cs.IR、cs.CL

Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain

Antoine Louis, Gijs van Dijck, Gerasimos Spanakis

专题命中 检索器与排序 ：hybrid retrieval(title)；分类 cs.IR、cs.CL

Comments Under review

StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation

Davit Abrahamyan, Fatemeh H. Fard

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

DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning

Yuxi Feng, Raymond Li, Zhenan Fan, Giuseppe Carenini, Mohammadreza Pourreza, Weiwei Zhang, Yong Zhang

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

Enhancing Long-Term Memory using Hierarchical Aggregate Tree for Retrieval Augmented Generation

Aadharsh Aadhithya A, Sachin Kumar S, Soman K. P

专题命中 检索器与排序 ：retrieval augmented generation(title)；分类 cs.CL、cs.AI

Comments 6 pages, 2 figures

PLAID SHIRTTT for Large-Scale Streaming Dense Retrieval

Dawn Lawrie, Efsun Kayi, Eugene Yang, James Mayfield, Douglas W. Oard

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

Comments 5 pages, 1 figure, accepted at SIGIR 2024 as short paper

Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation

Eugene Yang, Dawn Lawrie, James Mayfield, Douglas W. Oard, Scott Miller

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

Comments 17 pages, 1 figure, accepted at ECIR 2024

LEAD: Liberal Feature-based Distillation for Dense Retrieval

Hao Sun, Xiao Liu, Yeyun Gong, Anlei Dong, Jingwen Lu, Yan Zhang, Linjun Yang, Rangan Majumder, Nan Duan

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

Comments Accepted by WSDM 2024

Noisy Self-Training with Synthetic Queries for Dense Retrieval

Fan Jiang, Tom Drummond, Trevor Cohn

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

Comments Accepted by EMNLP 2023 Findings

FloodBrain: Flood Disaster Reporting by Web-based Retrieval Augmented Generation with an LLM

Grace Colverd, Paul Darm, Leonard Silverberg, Noah Kasmanoff

专题命中 检索器与排序 ：retrieval augmented generation(title)；分类 cs.CL、cs.AI

Comments Version is the one submitted to Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop @Neurips2023. All authors contributed equally to this work

MWPRanker: An Expression Similarity Based Math Word Problem Retriever

Mayank Goel, Venktesh V, Vikram Goyal

专题命中 检索器与排序 ：retriever(title)；分类 cs.IR、cs.AI

Comments Accepted to ECML-PKDD 2023

Query Encoder Distillation via Embedding Alignment is a Strong Baseline Method to Boost Dense Retriever Online Efficiency

Yuxuan Wang, Hong Lyu

专题命中 检索器与排序 ：retriever(title)；分类 cs.IR、cs.AI

Comments Accepted by the 4th SustaiNLP workshop at ACL 2023

Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training

Yibin Lei, Liang Ding, Yu Cao, Changtong Zan, Andrew Yates, Dacheng Tao

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

Comments ACL 2023 Findings (Short), 5 pages main + 1 page references + 1 page appendix

UnifieR: A Unified Retriever for Large-Scale Retrieval

Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Guodong Long, Kai Zhang, Daxin Jiang

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

Comments To appear at KDD ADS 2023

Soft Prompt Decoding for Multilingual Dense Retrieval

Zhiqi Huang, Hansi Zeng, Hamed Zamani, James Allan

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

Decoding a Neural Retriever's Latent Space for Query Suggestion

Leonard Adolphs, Michelle Chen Huebscher, Christian Buck, Sertan Girgin, Olivier Bachem, Massimiliano Ciaramita, Thomas Hofmann

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

Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training

Shengyao Zhuang, Guido Zuccon

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

Comments Demo paper, accepted at SIGIR2022

Condenser: a Pre-training Architecture for Dense Retrieval

Luyu Gao, Jamie Callan

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

Comments EMNLP 2021

Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval

Hongyin Tang, Xingwu Sun, Beihong Jin, Jingang Wang, Fuzheng Zhang, Wei Wu

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

Comments 11 pages, 2 figures, Accepted by ACL 2021

Domain-matched Pre-training Tasks for Dense Retrieval

Barlas Oğuz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Wen-tau Yih, Sonal Gupta, Yashar Mehdad

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

Unstructured Knowledge Access in Task-oriented Dialog Modeling using Language Inference, Knowledge Retrieval and Knowledge-Integrative Response Generation

Mudit Chaudhary, Borislav Dzodzo, Sida Huang, Chun Hei Lo, Mingzhi Lyu, Lun Yiu Nie, Jinbo Xing, Tianhua Zhang, Xiaoying Zhang, Jingyan Zhou, Hong Cheng, Wai Lam, Helen Meng

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

A Hybrid Retrieval-Generation Neural Conversation Model

Liu Yang, Junjie Hu, Minghui Qiu, Chen Qu, Jianfeng Gao, W. Bruce Croft, Xiaodong Liu, Yelong Shen, Jingjing Liu

专题命中 检索器与排序 ：hybrid retrieval(title)；分类 cs.IR、cs.CL

Comments Accepted as a Full Paper in CIKM 2019. 10 pages

Local Hybrid Retrieval-Augmented Document QA

本地混合检索增强文档问答

Paolo Astrino

机构 * Università Ca’ Foscari Venezia（威尼斯大学） 纠错

专题命中 检索器与排序 ：hybrid retrieval(title)；分类 cs.CL；RAG(comments)

AI总结 本研究提出一种本地混合检索增强的文档问答系统，通过结合语义理解和关键词精度，在不传输数据的情况下实现高准确率的问答，平衡隐私与性能。

Comments 10 pages, 5 figures, 3 tables; conference-style (ACL format); fully local RAG system

P-PAS: Prefill-Pressure Adaptive Scheduling for Long-Context LLM Serving

P-PAS：面向长上下文大语言模型服务的预填充压力自适应调度

Timo Sämann

专题命中 检索器与排序 ：RAG(abstract,abstract_cn)；retrieval-augmented generation(abstract)

AI总结 针对长上下文LLM服务中静态最大批处理令牌数（MBT）无法适配不同调度压力的问题，提出P-PAS动态调度策略，可在各类负载下维持低延迟，避免固定MBT的局限。

Model-agnostic Retrieval-Augmented Extended Forecasting for time series

模型无关的检索增强扩展预测用于时间序列

Juan Pablo Villa Serna, Rohan Asthana, Vasileios Belagiannis

专题命中 检索器与排序 ：RAG(abstract,abstract_cn)；retrieval augmented generation(abstract)

AI总结 该研究提出模型无关的RAEF方法，通过改进检索与聚合机制提升时间序列预测性能，其效果优于RAF，兼具高效性与竞争力，可替代微调用于时间序列预测的领域适配。

Comments 6 pages, 1 figure