https://github.com/EpistasisLab/pennai
공식 코드Evaluating recommender systems for AI-driven biomedical informatics
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Evaluating recommender systems for AI-driven biomedical informatics
Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase Extraction
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference
Structured Dialogue System for Mental Health: An LLM Chatbot Leveraging the PM+ Guidelines
EvalCrafter: Benchmarking and Evaluating Large Video Generation Models
GRATR: Zero-Shot Evidence Graph Retrieval-Augmented Trustworthiness Reasoning
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EgyBERT: A Large Language Model Pretrained on Egyptian Dialect Corpora
Large Language Model for Multi-objective Evolutionary Optimization
HonestLLM: Toward an Honest and Helpful Large Language Model
KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation
Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
Searching for Best Practices in Retrieval-Augmented Generation
PC Agent: While You Sleep, AI Works -- A Cognitive Journey into Digital World
Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation
Cognify: Supercharging Gen-AI Workflows With Hierarchical Autotuning
SCOPE: Sign Language Contextual Processing with Embedding from LLMs
Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving
Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMs
VLTP: Vision-Language Guided Token Pruning for Task-Oriented Segmentation
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild
Self-Evaluation of Large Language Model based on Glass-box Features
Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perception
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.