https://github.com/dosonleung/fasttog
공식 코드Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph
Reproducing sensory induced hallucinations via neural fields
Beyond Ontology in Dialogue State Tracking for Goal-Oriented Chatbot
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools
Tackling Structural Hallucination in Image Translation with Local Diffusion
R2-KG: General-Purpose Dual-Agent Framework for Reliable Reasoning on Knowledge Graphs
Factored Verification: Detecting and Reducing Hallucination in Summaries of Academic Papers
A Diffusion-Based Generative Equalizer for Music Restoration
Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making
Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making
Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making
MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations
Retrieval-Augmented Language Model for Extreme Multi-Label Knowledge Graph Link Prediction
CiteBART: Learning to Generate Citations for Local Citation Recommendation
Transferable Decoding with Visual Entities for Zero-Shot Image Captioning
LLMs and Memorization: On Quality and Specificity of Copyright Compliance
Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
One-for-More: Continual Diffusion Model for Anomaly Detection
Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning
The 2nd FutureDial Challenge: Dialog Systems with Retrieval Augmented Generation (FutureDial-RAG)
Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models
Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.