Retrieval Augmented Generation
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge sources to improve accuracy and address limitations like hallucinations. Current research focuses on optimizing retrieval strategies (e.g., using hierarchical graphs, attention mechanisms, or determinantal point processes for diverse and relevant information selection), improving the integration of retrieved information with LLM generation (e.g., through various prompting techniques and model adaptation methods), and mitigating biases and ensuring fairness in RAG systems. The impact of RAG is significant, improving performance on various tasks like question answering and enabling more reliable and contextually aware applications across diverse domains, including healthcare and scientific research.
Papers
Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents
Avital Shafran, Roei Schuster, Vitaly Shmatikov
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation
Kiseung Kim, Jay-Yoon Lee
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation
Shuting Wang, Jiongnan Liu, Shiren Song, Jiehan Cheng, Yuqi Fu, Peidong Guo, Kun Fang, Yutao Zhu, Zhicheng Dou
Multi-Head RAG: Solving Multi-Aspect Problems with LLMs
Maciej Besta, Ales Kubicek, Roman Niggli, Robert Gerstenberger, Lucas Weitzendorf, Mingyuan Chi, Patrick Iff, Joanna Gajda, Piotr Nyczyk, Jürgen Müller, Hubert Niewiadomski, Marcin Chrapek, Michał Podstawski, Torsten Hoefler
CRAG -- Comprehensive RAG Benchmark
Xiao Yang, Kai Sun, Hao Xin, Yushi Sun, Nikita Bhalla, Xiangsen Chen, Sajal Choudhary, Rongze Daniel Gui, Ziran Will Jiang, Ziyu Jiang, Lingkun Kong, Brian Moran, Jiaqi Wang, Yifan Ethan Xu, An Yan, Chenyu Yang, Eting Yuan, Hanwen Zha, Nan Tang, Lei Chen, Nicolas Scheffer, Yue Liu, Nirav Shah, Rakesh Wanga, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential
Wei Tang, Yixin Cao, Jiahao Ying, Bo Wang, Yuyue Zhao, Yong Liao, Pengyuan Zhou
Retrieval Augmented Generation in Prompt-based Text-to-Speech Synthesis with Context-Aware Contrastive Language-Audio Pretraining
Jinlong Xue, Yayue Deng, Yingming Gao, Ya Li
RATT: A Thought Structure for Coherent and Correct LLM Reasoning
Jinghan Zhang, Xiting Wang, Weijieying Ren, Lu Jiang, Dongjie Wang, Kunpeng Liu
Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor
Chuankai Xu, Dongming Zhao, Bo Wang, Hanwen Xing
RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts
Jing Yang, Xiao Wang, Yu Zhao, Yuhang Liu, Fei-Yue Wang
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination
Luyao Shi, Michael Kazda, Bradley Sears, Nick Shropshire, Ruchir Puri
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation
Kun Zhu, Xiaocheng Feng, Xiyuan Du, Yuxuan Gu, Weijiang Yu, Haotian Wang, Qianglong Chen, Zheng Chu, Jingchang Chen, Bing Qin
Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin
Carolina Fortuna, Vid Hanžel, Blaž Bertalanič
Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs
Fatemeh Shiri, Van Nguyen, Farhad Moghimifar, John Yoo, Gholamreza Haffari, Yuan-Fang Li
A Theory for Token-Level Harmonization in Retrieval-Augmented Generation
Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng
BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models
Jiaqi Xue, Mengxin Zheng, Yebowen Hu, Fei Liu, Xun Chen, Qian Lou