공대생 도전 일지

RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation - 논문 리뷰

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..

GeAR: Generation Augmented Retrieval - 논문리뷰

https://arxiv.org/abs/2501.02772 GeAR: Generation Augmented RetrievalDocument retrieval techniques form the foundation for the development of large-scale information systems. The prevailing methodology is to construct a bi-encoder and compute the semantic similarity. However, such scalar similarity is difficult to reflect earxiv.org 현재 존재하는 Bi-Encoder를 통한 유사도 계산은 정보를 충분히 반영하기 어렵고, 이해하기도 어렵다. 또한 ..