https://github.com/youngdubbydu/llm-agent-optimization
공식 코드A Survey on the Optimization of Large Language Model-based Agents
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
A Survey on the Optimization of Large Language Model-based Agents
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation
RAGPPI: RAG Benchmark for Protein-Protein Interactions in Drug Discovery
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
Sequential Recommendation with Latent Relations based on Large Language Model
PairAug: What Can Augmented Image-Text Pairs Do for Radiology?
Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel Decoding
Introducing Visual Perception Token into Multimodal Large Language Model
R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning
Soundscape Captioning using Sound Affective Quality Network and Large Language Model
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
Where, What, Why: Towards Explainable Driver Attention Prediction
TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphs
Control Industrial Automation System with Large Language Model Agents
SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
A Semantic Partitioning Method for Large-Scale Training of Knowledge Graph Embeddings
From Text to Pixel: Advancing Long-Context Understanding in MLLMs
Constructing Temporal Dynamic Knowledge Graphs from Interactive Text-based Games
Distantly-Supervised Joint Extraction with Noise-Robust Learning
LMEye: An Interactive Perception Network for Large Language Models
How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.