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
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs
Xin Zhou, Ping Nie, Yiwen Guo, Haojie Wei, Zhanqiu Zhang, Pasquale Minervini, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang
BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression
Yuankai Li, Jia-Chen Gu, Di Wu, Kai-Wei Chang, Nanyun Peng
When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
Shang Wang, Tianqing Zhu, Dayong Ye, Wanlei Zhou
Class-RAG: Content Moderation with Retrieval Augmented Generation
Jianfa Chen, Emily Shen, Trupti Bavalatti, Xiaowen Lin, Yongkai Wang, Shuming Hu, Harihar Subramanyam, Ksheeraj Sai Vepuri, Ming Jiang, Ji Qi, Li Chen, Nan Jiang, Ankit Jain
Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases
Elias Lumer
RAG-ConfusionQA: A Benchmark for Evaluating LLMs on Confusing Questions
Zhiyuan Peng, Jinming Nian, Alexandre Evfimievski, Yi Fang
Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models
Cody Clop, Yannick Teglia
SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques
Qiming Guo, Jinwen Tang, Wenbo Sun, Haoteng Tang, Yi Shang, Wenlu Wang
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems
Nandan Thakur, Suleman Kazi, Ge Luo, Jimmy Lin, Amin Ahmad
HEALTH-PARIKSHA: Assessing RAG Models for Health Chatbots in Real-World Multilingual Settings
Varun Gumma, Anandhita Raghunath, Mohit Jain, Sunayana Sitaram
Integrating Temporal Representations for Dynamic Memory Retrieval and Management in Large Language Models
Yuki Hou, Haruki Tamoto, Homei Miyashita
RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards
Xinze Li, Sen Mei, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng, Hao Chen, Ge Yu, Zhiyuan Liu, Maosong Sun, Chenyan Xiong
Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval
Ingeol Baek, Hwan Chang, Byeongjeong Kim, Jimin Lee, Hwanhee Lee
SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation
Prakhar Dixit, Tim Oates
A Systematic Investigation of Knowledge Retrieval and Selection for Retrieval Augmented Generation
Xiangci Li, Jessica Ouyang
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models
Jiatao Li, Xinyu Hu, Xunjian Yin, Xiaojun Wan
Is Semantic Chunking Worth the Computational Cost?
Renyi Qu, Ruixuan Tu, Forrest Bao
Meta-Chunking: Learning Efficient Text Segmentation via Logical Perception
Jihao Zhao, Zhiyuan Ji, Pengnian Qi, Simin Niu, Bo Tang, Feiyu Xiong, Zhiyu Li
CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity
Jintao Liu, Ruixue Ding, Linhao Zhang, Pengjun Xie, Fie Huang