https://github.com/yuan-li-fnlp/r3-rag
공식 코드R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning
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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
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
Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervision
LLMQuoter: Enhancing RAG Capabilities Through Efficient Quote Extraction From Large Contexts
HALO: Half Life-Based Outdated Fact Filtering in Temporal Knowledge Graphs
DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery
Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
PoisonArena: Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation
TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning
KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations
Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.