RAG / 检索增强生成

DRUM: Learning Demonstration Retriever for Large MUlti-modal Models

Ellen Yi-Ge, Jiechao Gao, Wei Han, Wei Zhu

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

Inference is All You Need: Self Example Retriever for Cross-domain Dialogue State Tracking with ChatGPT

Jihyun Lee, Gary Geunbae Lee

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

Measuring text summarization factuality using atomic facts entailment metrics in the context of retrieval augmented generation

N. E. Kriman

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

Comments 12 pages

FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG

Sai Puppala, Ismail Hossain, Md Jahangir Alam, Sajedul Talukder

专题命中 检索器与排序 ：RAG(title)；分类 cs.IR

Comments This research paper is submitted to ASONAM 2024 conference on Advances in Social Networks Analysis and Mining and going to be published in Springer

Accuracy and Consistency of LLMs in the Registered Dietitian Exam: The Impact of Prompt Engineering and Knowledge Retrieval

Iman Azimi, Mohan Qi, Li Wang, Amir M. Rahmani, Youlin Li

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

Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation

Xinyu Mao, Shengyao Zhuang, Bevan Koopman, Guido Zuccon

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

Comments Accepted at SIGIR 2024;typos corrected

Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models

Yuzhe Zhang, Yipeng Zhang, Yidong Gan, Lina Yao, Chen Wang

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

Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval

Mengjia Niu, Hao Li, Jie Shi, Hamed Haddadi, Fan Mo

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

MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval

Debopriyo Banerjee, Mausam Jain, Ashish Kulkarni

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

Comments The first two authors contributed equally to this work. 12 pages, 3 figures, 5 tables. Accepted at the 2023 Pacific-Asia Conference On Knowledge Discovery And Data Mining (PAKDD)

K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries

Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayanan, Manas Gaur

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

Comments Accepted at AAAI 2024 Spring Symposium Series

Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction

Qi Sun, Kun Huang, Xiaocui Yang, Rong Tong, Kun Zhang, Soujanya Poria

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

Comments Accepted by WWW 2024

DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever

Zhichao Yin, Binyuan Hui, Min Yang, Fei Huang, Yongbin Li

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

Comments ICASSP 2024

DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning

Praveen Venkateswaran, Evelyn Duesterwald, Vatche Isahagian

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

An Analysis of Fusion Functions for Hybrid Retrieval

Sebastian Bruch, Siyu Gai, Amir Ingber

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

Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval

Houxing Ren, Linjun Shou, Jian Pei, Ning Wu, Ming Gong, Daxin Jiang

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

Comments EMNLP 2022 Findings

Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models

Tao Chen, Mingyang Zhang, Jing Lu, Michael Bendersky, Marc Najork

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

Comments Accepted at ECIR 2022 (full paper)

Knowledge Retrieval Using Functional Object-Oriented Networks

Gabriel Laverghetta

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

Comments 4 pages, 3 figures, 3 tables

Knowledge Retrieval for Robotic Cooking

Kundana Mandapaka

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

Comments 3 pages, 1 figure, and 2 tables; modified references

Knowledge Retrieval using Functional Object-Oriented Network

Naseem Shaik

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

Knowledge Retrieval

Vishnu Vardhan Reddy Palli

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

Comments 5 pages, 5 figures

Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer

Yujia Zhou, Jing Yao, Zhicheng Dou, Ledell Wu, Peitian Zhang, Ji-Rong Wen

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

Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever

Jin Chen, Defu Lian, Yucheng Li, Baoyun Wang, Kai Zheng, Enhong Chen

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

Comments 18 pages

Retrieval-Augmented Generation for Code Summarization via Hybrid GNN

Shangqing Liu, Yu Chen, Xiaofei Xie, Jingkai Siow, Yang Liu

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

OCoR: An Overlapping-Aware Code Retriever

Qihao Zhu, Zeyu Sun, Xiran Liang, Yingfei Xiong, Lu Zhang

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

Journal ref ASE 2020: 35th IEEE/ACM International Conference on Automated Software Engineering Proceedings

Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever

Libo Qin, Yijia Liu, Wanxiang Che, Haoyang Wen, Yangming Li, Ting Liu

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

Comments Accepted at EMNLP 2019

PsychoAgent: An Affect-Sensitive Cognitive Architecture for Conflict-Aware Memory in LLM Agents

PsychoAgent：面向大语言模型智能体的、具备冲突感知记忆的情感敏感认知架构

Mohammad Amanlou, Parham Abed Azad, Farbod Davoodi, Mostafa Masumi, Behnam Bahrak, Abdol-Hossein Vahabie

机构 * School of Electrical and Computer Engineering, College of Engineering, University of Tehran（德黑兰大学工程学院电气与计算机工程学院） ； Sharif University of Technology（谢里夫理工大学） ； Missouri University of Science and Technology（密苏里科技大学） ； Tehran Institute for Advanced Studies, Khatam University（哈塔姆大学德黑兰高等研究院） 纠错

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

AI总结 该研究提出PsychoAgent架构，分离事实与情感记忆并通过冲突感知控制器整合，在三类冲突场景中其冲突关键记忆检索率优于相关基线，为LLM智能体建模类人冲突效应提供了可检查机制。

Comments 12 pages main paper + 10 pages supplementary material; supplementary material included

Beyond "What to Retrieve": Uncertainty in Retrieval-Augmented Code Generation

超越“检索什么”：检索增强代码生成中的不确定性

Chandan Kumar Sah, Li Zhang, Xiaoli Lian

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

AI总结 研究存储库级代码生成中异构证据不确定性问题，提出不确定性感知框架OpenCoder，通过估计特定源不确定性来过滤和排序证据以指导相关操作，实验表明其能提高输出正确性，支持将不确定性作为可操作控制信号。

Comments 9 pages, 4 figures. Source code and supporting materials are available at https://github.com/Rocky5502/OpenCoder_V1

Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA

容量之上的数据质量：将文档内化到LoRA适配器中用于闭卷问答

Joan Figuerola Hurtado

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

AI总结 研究将文档内化到LoRA适配器用于闭卷问答，发现适配器容量足够时，训练数据质量主导闭卷准确率，一次整理大幅提升准确率，确认容量趋势及秩与学习率耦合，内化适配器在低延迟下表现优于检索基线，还报告了调试LLM训练的过程。

Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents

数字万神殿：使用大语言模型智能体模拟和审计联盟形成

Dylan Van Mulders, Matthias Bogaert, Dirk Van den Poel

机构 * Ghent University（根特大学） 纠错

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

AI总结 该研究针对政治联盟形成谈判，提出结合多种技术的多智能体框架，在弗拉芒选举中实施，通过引入相关拓扑、分数和检验使其可解释，模拟产生稳定结果，能可靠预测现实，提供了探索政党兼容性等的测试平台。

Comments 11 pages, 2 figures, 5 tables, To be published in the AIDEM Workshop proceedings of the ECML PKDD 2026 Conference

Bridge Evidence: Static Retrieval Utility Does Not Predict Causal Utility in Multi-Step Agentic Search

桥梁证据：静态检索效用无法预测多步智能体搜索中的因果效用

Debayan Mukhopadhyay, Utshab Kumar Ghosh, Shubham Chatterjee

机构 * University of Calcutta（卡塔克大学） ； Missouri University of Science and Technology（密苏里科技大学） 纠错

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

AI总结 研究智能体检索中静态检索效用与因果效用的差异，通过在HotpotQA上用ReAct风格智能体实验，比较原始与反事实运行得CTU分数，发现两者近乎独立，约三分之一文档为桥梁文档，还确定了相关机制，指出优化静态相关性无法带来因果有用性。

Comments Preprint; extended version in preparation