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Methods/BART

BART

SequentialIntroduced 20001642 papers
Source Paper

Description

BART is a denoising autoencoder for pretraining sequence-to-sequence models. It is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Transformer-based neural machine translation architecture. It uses a standard seq2seq/NMT architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT). This means the encoder's attention mask is fully visible, like BERT, and the decoder's attention mask is causal, like GPT2.

Papers Using This Method

Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker2025-07-16Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss2025-07-15LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning2025-07-15Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis2025-07-14The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover2025-07-09Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning2025-07-09SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression2025-07-08Knowledge Protocol Engineering: A New Paradigm for AI in Domain-Specific Knowledge Work2025-07-03CyberRAG: An agentic RAG cyber attack classification and reporting tool2025-07-03Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems2025-06-28ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation2025-06-27Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality2025-06-26PsyLite Technical Report2025-06-26EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora2025-06-26Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation2025-06-26AI Assistants to Enhance and Exploit the PETSc Knowledge Base2025-06-25CCRS: A Zero-Shot LLM-as-a-Judge Framework for Comprehensive RAG Evaluation2025-06-25Knowledge-Aware Diverse Reranking for Cross-Source Question Answering2025-06-25Memento: Note-Taking for Your Future Self2025-06-25Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation2025-06-25