https://github.com/zhiyuanpeng/eloq
공식 코드ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope Questions
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ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope Questions
CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction Alignment
Goal-Aware Identification and Rectification of Misinformation in Multi-Agent Systems
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models
ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems
LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT
B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens
RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment
WIKIGENBENCH: Exploring Full-length Wikipedia Generation under Real-World Scenario
Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces
Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents
LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning
Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability
AIstorian lets AI be a historian: A KG-powered multi-agent system for accurate biography generation
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
Reliable Academic Conference Question Answering: A Study Based on Large Language Model
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
Start from Zero: Triple Set Prediction for Automatic Knowledge Graph Completion
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.