https://github.com/iu-aes-ai4code/codequestionanswering
공식 코드Leveraging Large Language Models in Code Question Answering: Baselines and Issues
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
Leveraging Large Language Models in Code Question Answering: Baselines and Issues
Shallow decision-making analysis in General Video Game Playing
InterpreTabNet: Distilling Predictive Signals from Tabular Data by Salient Feature Interpretation
Ensemble Semi-supervised Entity Alignment via Cycle-teaching
Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods
Localized Symbolic Knowledge Distillation for Visual Commonsense Models
Localized Symbolic Knowledge Distillation for Visual Commonsense Models
ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding
Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents
Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy Planning
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns
ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles
Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training
Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion
The Digitalization of Bioassays in the Open Research Knowledge Graph
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected Compressor
SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation
AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations
Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation
Inductive Relation Prediction from Relational Paths and Context with Hierarchical Transformers
Collaborative Expert LLMs Guided Multi-Objective Molecular Optimization
ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification
CellTypeAgent: Trustworthy cell type annotation with Large Language Models
Item-side Fairness of Large Language Model-based Recommendation System
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.