RAG / 检索增强生成

MutaGReP: Execution-Free Repository-Grounded Plan Search for Code-Use

Zaid Khan, Ali Farhadi, Ranjay Krishna, Luca Weihs, Mohit Bansal, Tanmay Gupta

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

Comments Project page: zaidkhan.me/MutaGReP

EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts

Subhajit Chaudhury, Payel Das, Sarathkrishna Swaminathan, Georgios Kollias, Elliot Nelson, Khushbu Pahwa, Tejaswini Pedapati, Igor Melnyk, Matthew Riemer

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

Extracting Sentence Embeddings from Pretrained Transformer Models

Lukas Stankevičius, Mantas Lukoševičius

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

Comments Postprint update

Journal ref Appl. Sci. 2024, 14(19), 8887

Adaptive In-Context Learning with Large Language Models for Bundle Generation

Zhu Sun, Kaidong Feng, Jie Yang, Xinghua Qu, Hui Fang, Yew-Soon Ong, Wenyuan Liu

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

Comments Accepted by SIGIR 2024

Journal ref The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2024

Few-shot LLM Synthetic Data with Distribution Matching

Jiyuan Ren, Zhaocheng Du, Zhihao Wen, Qinglin Jia, Sunhao Dai, Chuhan Wu, Zhenhua Dong

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

Comments 10 pages, 5 figures, accepted at www 2025

A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia

Giovanni Monea, Maxime Peyrard, Martin Josifoski, Vishrav Chaudhary, Jason Eisner, Emre Kıcıman, Hamid Palangi, Barun Patra, Robert West

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

Comments Accepted at ACL 2024 (main conference)

Learning vs Retrieval: The Role of In-Context Examples in Regression with Large Language Models

Aliakbar Nafar, Kristen Brent Venable, Parisa Kordjamshidi

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

Aligning Large Language Models for Enhancing Psychiatric Interviews Through Symptom Delineation and Summarization: Pilot Study

Jae-hee So, Joonhwan Chang, Eunji Kim, Junho Na, JiYeon Choi, Jy-yong Sohn, Byung-Hoon Kim, Sang Hui Chu

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

Journal ref JMIR Form Res 2024;8:e58418

LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge

Shaswata Mitra, Subash Neupane, Trisha Chakraborty, Sudip Mittal, Aritran Piplai, Manas Gaur, Shahram Rahimi

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

Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions

Hongru Wang, Boyang Xue, Baohang Zhou, Tianhua Zhang, Cunxiang Wang, Huimin Wang, Guanhua Chen, Kam-fai Wong

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

CoT-Driven Framework for Short Text Classification: Enhancing and Transferring Capabilities from Large to Smaller Model

Hui Wu, Yuanben Zhang, Zhonghe Han, Yingyan Hou, Lei Wang, Siye Liu, Qihang Gong, Yunping Ge

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

Comments Knowledge-Based Systems

Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMs

Reham Omar, Omij Mangukiya, Essam Mansour

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

Comments The paper is publsihed in SIGMOD 2025

ListConRanker: A Contrastive Text Reranker with Listwise Encoding

Junlong Liu, Yue Ma, Ruihui Zhao, Junhao Zheng, Qianli Ma, Yangyang Kang

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

Comments 11 pages, 4 figures

Decoupling Knowledge and Reasoning in Transformers: A Modular Architecture with Generalized Cross-Attention

Zhenyu Guo, Wenguang Chen

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

An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems

Hashmath Shaik, Alex Doboli

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

Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp)

Jeongsu Yu

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

On the Suitability of pre-trained foundational LLMs for Analysis in German Legal Education

Lorenz Wendlinger, Christian Braun, Abdullah Al Zubaer, Simon Alexander Nonn, Sarah Großkopf, Christofer Fellicious, Michael Granitzer

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

Comments 11 pages

1-800-SHARED-TASKS at RegNLP: Lexical Reranking of Semantic Retrieval (LeSeR) for Regulatory Question Answering

Jebish Purbey, Drishti Sharma, Siddhant Gupta, Khawaja Murad, Siddartha Pullakhandam, Ram Mohan Rao Kadiyala

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

Comments 5 pages, Accepted to RegNLP @ COLING 2025

Language Model Powered Digital Biology with BRAD

Joshua Pickard, Ram Prakash, Marc Andrew Choi, Natalie Oliven, Cooper Stansbury, Jillian Cwycyshyn, Alex Gorodetsky, Alvaro Velasquez, Indika Rajapakse

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

Comments 12 pages, 3 figures, 1 table. See: https://github.com/Jpickard1/BRAD

Teaching Smaller Language Models To Generalise To Unseen Compositional Questions (Full Thesis)

Tim Hartill

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

Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios

Shaochen Xu, Yifan Zhou, Zhengliang Liu, Zihao Wu, Tianyang Zhong, Huaqin Zhao, Yiwei Li, Hanqi Jiang, Yi Pan, Junhao Chen, Jin Lu, Wei Zhang, Tuo Zhang, Lu Zhang, Dajiang Zhu, Xiang Li, Wei Liu, Quanzheng Li, Andrea Sikora, Xiaoming Zhai, Zhen Xiang, Tianming Liu

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

AddrLLM: Address Rewriting via Large Language Model on Nationwide Logistics Data

Qinchen Yang, Zhiqing Hong, Dongjiang Cao, Haotian Wang, Zejun Xie, Tian He, Yunhuai Liu, Yu Yang, Desheng Zhang

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

Comments Accepted by KDD'25 ADS Track

Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events

Yuanyuan Tian, Wenwen Li, Lei Hu, Xiao Chen, Michael Brook, Michael Brubaker, Fan Zhang, Anna K. Liljedahl

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

Automated Clinical Data Extraction with Knowledge Conditioned LLMs

Diya Li, Asim Kadav, Aijing Gao, Rui Li, Richard Bourgon

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

Comments COLING25 Industry Track

How Does the Textual Information Affect the Retrieval of Multimodal In-Context Learning?

Yang Luo, Zangwei Zheng, Zirui Zhu, Yang You

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

Comments EMNLP 2024

Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers

Sheshera Mysore, Zhuoran Lu, Mengting Wan, Longqi Yang, Bahareh Sarrafzadeh, Steve Menezes, Tina Baghaee, Emmanuel Barajas Gonzalez, Jennifer Neville, Tara Safavi

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

Comments Accepted to Workshop on Customizable NLP at EMNLP 2024

Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models

Aashiq Muhamed, Mona Diab, Virginia Smith

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

What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration

Libo Qin, Qiguang Chen, Hao Fei, Zhi Chen, Min Li, Wanxiang Che

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

Comments Accepted at NeurIPS 2024

Historical Test-time Prompt Tuning for Vision Foundation Models

Jingyi Zhang, Jiaxing Huang, Xiaoqin Zhang, Ling Shao, Shijian Lu

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

Comments NeurIPS 2024 Camera Ready

README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP

Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei, Hieu Tran, Zhangqi Duan, Sunjae Kwon, Zhichao Yang, README annotation team, Hong Yu

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

Comments To appear in Findings of the Association for Computational Linguistics: EMNLP 2024. We sincerely appreciate the tremendous efforts of the entire README annotation team throughout the expert annotation process of the README dataset