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https://arxiv.org/abs/2501.09749 Enhancing Lexicon-Based Text Embeddings with Large Language ModelsRecent large language models (LLMs) have demonstrated exceptional performance on general-purpose text embedding tasks. While dense embeddings have dominated related research, we introduce the first Lexicon-based EmbeddiNgS (LENS) leveraging LLMs that achiearxiv.org기존 Dense embedding의 문제점을 말합니다.그리고 ..

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..