https://github.com/halueval-wild/halueval-wild
공식 코드HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild
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.
HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild
MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning
AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric Knowledge
Specious Sites: Tracking the Spread and Sway of Spurious News Stories at Scale
LLM-SAP: Large Language Models Situational Awareness Based Planning
DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration
Knowledge Graph-Enhanced Large Language Models via Path Selection
Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question Answering
mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data
CMMLU: Measuring massive multitask language understanding in Chinese
Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction
Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction
VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
"All that Glitters": Approaches to Evaluations with Unreliable Model and Human Annotations
Toward Degree Bias in Embedding-Based Knowledge Graph Completion
Learning Representations for Hyper-Relational Knowledge Graphs
Distance-Based Propagation for Efficient Knowledge Graph Reasoning
AdaptVision: Dynamic Input Scaling in MLLMs for Versatile Scene Understanding
Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs
Streamlining the Collaborative Chain of Models into A Single Forward Pass in Generation-Based Tasks
Estimating Large Language Model Capabilities without Labeled Test Data
An Empirical Analysis of Uncertainty in Large Language Model Evaluations
Labels Generated by Large Language Model Helps Measuring People's Empathy in Vitro
An Open-World Extension to Knowledge Graph Completion Models
MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments
Enabling Scalable Evaluation of Bias Patterns in Medical LLMs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.