https://github.com/baichuan-inc/baichuan-audio
공식 코드Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction
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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.
Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction
Agentic Society: Merging skeleton from real world and texture from Large Language Model
EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Model
Persona Inconstancy in Multi-Agent LLM Collaboration: Conformity, Confabulation, and Impersonation
Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information
Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information
ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation
Establishing baselines for generative discovery of inorganic crystals
LLaSA: A Multimodal LLM for Human Activity Analysis Through Wearable and Smartphone Sensors
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification
Detecting Hallucinations in Large Language Model Generation: A Token Probability Approach
Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation
PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations
Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction
Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers
InGram: Inductive Knowledge Graph Embedding via Relation Graphs
PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
LLatrieval: LLM-Verified Retrieval for Verifiable Generation
A Survey of Multimodal Large Language Model from A Data-centric Perspective
FinTruthQA: A Benchmark Dataset for Evaluating the Quality of Financial Information Disclosure
Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation
Selective Text Augmentation with Word Roles for Low-Resource Text Classification
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data
PLeak: Prompt Leaking Attacks against Large Language Model Applications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.