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2025.03.17 - [인공지능/논문 리뷰 or 진행] - Embedding + Generation Model 사전 논문 조사5 - 데이터 셋 및 평가 데이터 정리 Embedding + Generation Model 사전 논문 조사5 - 데이터 셋 및 평가 데이터 정리2024.12.23 - [인공지능/논문 리뷰 or 진행] - ChatQA: Surpassing GPT-4 on Conversational QA and RAG - 논문 리뷰 ChatQA: Surpassing GPT-4 on Conversational QA and RAG - 논문 리뷰https://arxiv.org/abs/2401.10225 ChatQA: Surpassing GPT-4 onyoonschallenge.tistory.com여기서 ..

2024.12.23 - [인공지능/논문 리뷰 or 진행] - ChatQA: Surpassing GPT-4 on Conversational QA and RAG - 논문 리뷰 ChatQA: Surpassing GPT-4 on Conversational QA and RAG - 논문 리뷰https://arxiv.org/abs/2401.10225 ChatQA: Surpassing GPT-4 on Conversational QA and RAGIn this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-augmented generation (RAG) and conversational question answering (Q..

https://aclanthology.org/2025.coling-main.94/ Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language ModelsChengkai Huang, Yu Xia, Rui Wang, Kaige Xie, Tong Yu, Julian McAuley, Lina Yao. Proceedings of the 31st International Conference on Computational Linguistics. 2025.aclanthology.org그런데 이 논문은 Embedding + Gen은 아닌 잘 못 찾은 논문이라 ㅎㅎ... 그래도 재밌어서 쭉 읽어 봤습니다. 이 논문은 검색을 언제 진행할까가 주요..

https://arxiv.org/abs/2405.19648 Detecting Hallucinations in Large Language Model Generation: A Token Probability ApproachConcerns regarding the propensity of Large Language Models (LLMs) to produce inaccurate outputs, also known as hallucinations, have escalated. Detecting them is vital for ensuring the reliability of applications relying on LLM-generated content. Current mearxiv.org Hallucinat..

https://arxiv.org/abs/2412.11919 RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within GenerationLarge language models (LLMs) exhibit remarkable generative capabilities but often suffer from hallucinations. Retrieval-augmented generation (RAG) offers an effective solution by incorporating external knowledge, but existing methods still face several limarxiv.org  LLM은..

2025.02.25 - [인공지능/논문 리뷰 or 진행] - GRIT 생성과 Embedding을 동시에 Generative Representational Instruction Tuning - 논문 리뷰일단 시작은 이 논문이겠습니다.생성과 Embedding을 동시에 하는 모델이 있으면 좋겠다 싶었는데 2025 ICLR에 올라간 것 같네요 ㅎㅎ...그래서 이 논문을 인용한 다른 논문들을 확인해보면서 제가 무엇을 할 수 있을지, 좀 더 다른 점을 어떻게 만들 수 있을지 확인해보겠습니다. https://arxiv.org/abs/2403.20327 Gecko: Versatile Text Embeddings Distilled from Large Language ModelsWe present Gecko, a comp..

일단 RAG에 좋은 사이트를 발견해서 기록https://openrag.notion.site/Open-RAG-c41b2a4dcdea4527a7c1cd998e763595#6d4997a734a24a658fafcabb16684abe Open RAG | NotionAn open-source and open-access RAG platformopenrag.notion.site https://arxiv.org/abs/2410.13070 Is Semantic Chunking Worth the Computational Cost?Recent advances in Retrieval-Augmented Generation (RAG) systems have popularized semantic chunking, which aim..

https://arxiv.org/abs/2404.01037 ARAGOG: Advanced RAG Output GradingRetrieval-Augmented Generation (RAG) is essential for integrating external knowledge into Large Language Model (LLM) outputs. While the literature on RAG is growing, it primarily focuses on systematic reviews and comparisons of new state-of-the-art (SoTA)arxiv.org 이 논문은 RAG 기술을 체계적으로 비교하며, 검색 정확도와 답변 유사성이라는 명확한 지표를 통해 RAG 시스템의 성..

https://arxiv.org/abs/2402.05131 Financial Report Chunking for Effective Retrieval Augmented GenerationChunking information is a key step in Retrieval Augmented Generation (RAG). Current research primarily centers on paragraph-level chunking. This approach treats all texts as equal and neglects the information contained in the structure of documents. We proarxiv.org 일단 제가 찾던 논문 중 하나입니다!Chunking을..

https://arxiv.org/abs/2406.17526 LumberChunker: Long-Form Narrative Document SegmentationModern NLP tasks increasingly rely on dense retrieval methods to access up-to-date and relevant contextual information. We are motivated by the premise that retrieval benefits from segments that can vary in size such that a content's semantic independencearxiv.org 이 논문은 LLM을 통해 청크를 분리하네요그런데 이렇게 되면 리소스가 너무 과하..