공대생 도전 일지

https://arxiv.org/abs/2307.16789 ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIsDespite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to fulfill human instructions. The reason is that current instruction tuning laarxiv.org 이 논문은 API를 정리하여 GPT를 이용..

https://arxiv.org/abs/2501.05485 S2 Chunking: A Hybrid Framework for Document Segmentation Through Integrated Spatial and Semantic AnalysisDocument chunking is a critical task in natural language processing (NLP) that involves dividing a document into meaningful segments. Traditional methods often rely solely on semantic analysis, ignoring the spatial layout of elements, which is crucial forarxi..

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/1908.10084 Sentence-BERT: Sentence Embeddings using Siamese BERT-NetworksBERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes aarxiv.org RAG가 상용화 될 수 있었던 논문인 것 같습니다.기존 엄청나게 오..

https://arxiv.org/abs/2312.10997 Retrieval-Augmented Generation for Large Language Models: A SurveyLarge Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution byarxiv.org  이 논문도 서베이 논문이었습니다.RAG에 대한 조사를 진행..