RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots
Retrieval-Augmented Generation (RAG)은 외부 지식을 통합하여 환각을 줄일 수 있음을 입증하며, 신뢰할 수 있는 대화형 AI 모델을 개발하는 중요성을 강조한다.
Optimierung von Retrieval-Augmented Generation-Systemen: Eine umfassende Analyse
Der Erfolg von Retrieval-Augmented Generation (RAG) hängt stark von der Konfiguration des Systems ab. Die Autoren stellen ein Framework namens RAGGED vor, um RAG-Systeme zu analysieren und zu optimieren.
Comprehensive Evaluation and Reproduction of Retrieval-Augmented Generation Algorithms
RAGLAB is a modular and research-oriented open-source library that enables fair comparison of existing RAG algorithms and simplifies the development of novel RAG algorithms.
UncertaintyRAG: Enhancing Long-Context Retrieval-Augmented Generation with Span-Level Uncertainty for Improved Robustness and Generalization
UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG), leverages span-level uncertainty to enhance similarity estimation between text chunks, leading to improved model calibration, robustness, and generalization in long-context tasks.
Synchronous Faithfulness Monitoring and Decoding for Trustworthy Retrieval-Augmented Generation in Language Models
This paper introduces SYNCHECK, a novel method for monitoring and improving the faithfulness of retrieval-augmented language models (RALMs) in long-form generation tasks, ensuring that generated text aligns with provided context.
報酬駆動型教師あり学習によるRAGの強化：Reward-RAG
本稿で提案するReward-RAGは、報酬モデルとCriticGPTを用いることで、従来のRAGシステムにおける検索の質、特に関連性の向上と人間の好みに合わせた調整を実現する手法である。
基於獎勵驅動監督的增強型檢索增強生成模型：Reward-RAG
Reward-RAG 透過整合獎勵模型和 CriticGPT 來增強檢索增強生成 (RAG) 模型，從而提高生成文本的相關性和質量。
How Parametric and Non-parametric Memory Interact in Retrieval-Augmented Language Models: A Case Study of ATLAS
Retrieval-augmented language models like ATLAS rely heavily on retrieved context (non-parametric memory) over their own learned parameters (parametric memory) when answering questions, engaging in a two-step process of relevance evaluation followed by object extraction.
Enhancing the Robustness of Retrieval-Augmented Generation: A Corrective Approach
Retrieval-Augmented Generation (RAG) models are susceptible to hallucinations stemming from inaccurate retrieval results. This paper introduces Corrective Retrieval Augmented Generation (CRAG), a novel method to enhance the robustness of RAG by implementing a self-correction mechanism for retrieved documents and leveraging web searches for knowledge supplementation.
修正型檢索增強生成
大型語言模型 (LLM) 即使透過參數化知識也難以確保生成文本的準確性，而檢索增強生成 (RAG) 雖可作為補充，但高度依賴檢索文件的相關性。本研究提出修正型檢索增強生成 (CRAG)，透過評估檢索文件品質、觸發不同知識檢索動作（如網路搜尋）、精煉知識片段等方式，提升 RAG 的穩健性，並透過實驗證明 CRAG 能顯著提升 RAG 在短、長文本生成任務上的效能。
