https://github.com/templex98/mova
공식 코드MoVA: Adapting Mixture of Vision Experts to Multimodal Context
Papers with Code 아카이브
Knowledge Graph Completion
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Knowledge graphs $G$ are represented as a collection of triples $\\{(h, r, t)\\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/temporalkgteam/tango
공식 코드https://github.com/temporarylora/block-attention
공식 코드https://github.com/tencent-ailab/persona-hub
공식 코드https://github.com/tencent-ailab/tleague_projpage
공식 코드https://github.com/tencent/digitalhuman
공식 코드Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models
https://github.com/tencent/hunyuandit
공식 코드https://github.com/tencentarc/seed-story
공식 코드SEED-Story: Multimodal Long Story Generation with Large Language Model
https://github.com/tengxiao1/gsil
공식 코드https://github.com/tensorflow/neural-structured-learning
공식 코드https://github.com/terrierteam/pyterrier_rag
공식 코드Constructing and Evaluating Declarative RAG Pipelines in PyTerrier
https://github.com/terry-cyx/metaopenfoam
공식 코드https://github.com/teshnizi/optimus
공식 코드OptiMUS: Optimization Modeling Using MIP Solvers and large language models
https://github.com/tetis-nlp/landmatrix-graphql-python
공식 코드Adaptations of AI models for querying the LandMatrix database in natural language
https://github.com/tewiSong/Rot-Pro
공식 코드Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding
https://github.com/tgebhart/sheaf_kg_transind
공식 코드Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference
https://github.com/tgoldsack1/enhancing_biomedical_lay_summarisation_with_external_knowledge_graphs
공식 코드Enhancing Biomedical Lay Summarisation with External Knowledge Graphs
https://github.com/the-bird-F/GLM-Voice-RAG
공식 코드Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation
https://github.com/the-deep-nlp/bias-aware-humanitarian-entry-classification
공식 코드Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification
https://github.com/the-finai/pixiu
공식 코드FinBen: A Holistic Financial Benchmark for Large Language Models
https://github.com/the-martyr/causalmm
공식 코드https://github.com/thedatastation/pneuma
공식 코드Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System
https://github.com/themarcosf/mech-interp-paper
공식 코드Mechanistic Interpretability in the Presence of Architectural Obfuscation
https://github.com/thepurpleowl/codequeries-benchmark
공식 코드https://github.com/theroadqaq/relift
공식 코드Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions
https://github.com/theryanl/egs
공식 코드Improving Interpersonal Communication by Simulating Audiences with Language Models
https://github.com/thescreamingmonkey/mbm-em
공식 코드Simple and Interpretable Probabilistic Classifiers for Knowledge Graphs
https://github.com/theworldofagents/agentic-reasoning
공식 코드Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research
https://github.com/thezjd/aspectmmkg
공식 코드AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities
https://github.com/thijmennijdam/NevIR-reproducibility
공식 코드https://github.com/thinkwee/iAgents
공식 코드Autonomous Agents for Collaborative Task under Information Asymmetry
https://github.com/thisishwang/exit
공식 코드EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation
https://github.com/thiviyant/intelligraphs
공식 코드IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation
https://github.com/thomlake/investigating-alignment
공식 코드From Distributional to Overton Pluralism: Investigating Large Language Model Alignment
https://github.com/thu-bpm/watermark-radioactivity-attack
공식 코드Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?
https://github.com/thu-coai/JointGT
공식 코드JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.