공대생 도전 일지

DeepSeek-V3 Technical Report - 논문 리뷰

https://arxiv.org/abs/2412.19437 DeepSeek-V3 Technical ReportWe present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and Deeparxiv.org 화제의 모델입니다...저는 이 논문이 나왔을 때 화제가 되었어야 하지 않았나 생각했는데 너무 뒤늦게 R1모델이 나오고 나서 화제..

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model - 논문 리뷰

https://arxiv.org/abs/2405.04434 DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language ModelWe present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokearxiv.org 요즘 제일 화두가 되고 있는 딥..

MindAgent: Emergent Gaming Interaction - 논문 리뷰

https://arxiv.org/abs/2309.09971 MindAgent: Emergent Gaming InteractionLarge Language Models (LLMs) have the capacity of performing complex scheduling in a multi-agent system and can coordinate these agents into completing sophisticated tasks that require extensive collaboration. However, despite the introduction of numerousarxiv.org MINDAGENT 논문은 대규모 언어 모델(LLM)을 활용한 다중 에이전트 협업과 계획 능력을 체계적으로 평가한..

The Ability of Large Language Models to Evaluate Constraint-satisfaction in Agent Responses to Open-ended Requests - 논문 리뷰

https://arxiv.org/abs/2409.14371 The Ability of Large Language Models to Evaluate Constraint-satisfaction in Agent Responses to Open-ended RequestsGenerative AI agents are often expected to respond to complex user requests that have No One Right Answer (NORA), e.g., "design a vegetarian meal plan below 1800 calories". Such requests may entail a set of constraints that the agent should adhere to...

Generative Agents: Interactive Simulacra of Human Behavior - 논문 리뷰

https://arxiv.org/abs/2304.03442 Generative Agents: Interactive Simulacra of Human BehaviorBelievable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agarxiv.org 음 본인 스스로를 정의했다는 것을 이 논문의 중심으로 봐야 할지, 상대방과..

AutoAgents: A Framework for Automatic Agent Generation - 논문 리뷰

https://arxiv.org/abs/2309.17288 AutoAgents: A Framework for Automatic Agent GenerationLarge language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-aarxiv.org 이 논문은 기존 에이전트들이 고정된 시스탬에서 돌아가는 한계를 지적하고, 그 한계..

https://github.com/jina-ai/late-chunking GitHub - jina-ai/late-chunking: Code for explaining and evaluating late chunking (chunked pooling)Code for explaining and evaluating late chunking (chunked pooling) - jina-ai/late-chunkinggithub.com 일단 코드는 여기서 나왔습니다.코드에 익숙해지기 위해 조금 제맘대로 파 해치기도 했습니다.청크 풀링 (Chunked Pooling)그 다음으로, 우리가 임베딩에 사용할 모델을 로드합니다. 여기에서는 jinaai/jina-embeddings-v2-base-en을 선택했지만, 평균 풀링..

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..