https://github.com/AzusaXuan/AnnoDPO
공식 코드AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
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AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives
Biomedical knowledge graph-optimized prompt generation for large language models
AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases
Diachronic Embedding for Temporal Knowledge Graph Completion
Out-of-Sample Representation Learning for Multi-Relational Graphs
video-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model
A semantic embedding space based on large language models for modelling human beliefs
Knowledge Enhanced Multi-intent Transformer Network for Recommendation
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction
Large Language Model for Verilog Generation with Code-Structure-Guided Reinforcement Learning
OKGIT: Open Knowledge Graph Link Prediction with Implicit Types
Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes
Spatial-Temporal Large Language Model for Traffic Prediction
Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset
Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding
LLMSat: A Large Language Model-Based Goal-Oriented Agent for Autonomous Space Exploration
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts
Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding
PatchProt: Hydrophobic patch prediction using protein foundation models
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.