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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/allenai/natural-instructions
공식 코드Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/allenai/robustnav
공식 코드RobustNav: Towards Benchmarking Robustness in Embodied Navigation
https://github.com/allenai/wildbench
공식 코드WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild
https://github.com/allenai/wimbd
공식 코드https://github.com/alloydas/fast-textspotter
공식 코드FastTextSpotter: A High-Efficiency Transformer for Multilingual Scene Text Spotting
https://github.com/alpha-vllm/llama2-accessory
공식 코드SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
https://github.com/alphafin-proj/alphafin
공식 코드AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework
https://github.com/alsace08/sumcot
공식 코드https://github.com/altndrr/apex
공식 코드Automatic benchmarking of large multimodal models via iterative experiment programming
https://github.com/altoslabs/perturbench
공식 코드PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
https://github.com/alvin-zeng/temporal-robustness-benchmark
공식 코드Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions
https://github.com/amayuelas/NNKGReasoning
공식 코드https://github.com/amazon-research/question-answer-consolidation
공식 코드https://github.com/amazon-science/PrefEval
공식 코드Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs
https://github.com/amazon-science/dataflow-evaluation-toolkit
공식 코드https://github.com/amazon-science/e2e-docie
공식 코드REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
https://github.com/amazon-science/petshop-root-cause-analysis
공식 코드The PetShop Dataset -- Finding Causes of Performance Issues across Microservices
https://github.com/ameyagodbole/long-tail-likelihood-splits
공식 코드Benchmarking Long-tail Generalization with Likelihood Splits
https://github.com/amir-ni/trajectory-prediction
공식 코드TrajLearn: Trajectory Prediction Learning using Deep Generative Models
https://github.com/amnondrory/lidarregistration
공식 코드https://github.com/amr-keleg/dlama
공식 코드https://github.com/amrinkareem/paris3d
공식 코드PARIS3D: Reasoning-based 3D Part Segmentation Using Large Multimodal Model
https://github.com/amzn/emukit
공식 코드https://github.com/andiva/tmpnn
공식 코드TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization
https://github.com/andreea-glavan/multimodal-audiovisual-scene-recognition
공식 코드InstaIndoor and Multi-modal Deep Learning for Indoor Scene Recognition
https://github.com/andreeaiana/nemig
공식 코드NeMig -- A Bilingual News Collection and Knowledge Graph about Migration
https://github.com/andreeaiana/newsreclib
공식 코드NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation
https://github.com/andrepugni/esc
공식 코드https://github.com/andreskarjus/machineassistedmixedmethods
공식 코드https://github.com/andreyrisukhin/colorgrid
공식 코드ColorGrid: A Multi-Agent Non-Stationary Environment for Goal Inference and Assistance
https://github.com/andygr/ml-classification
공식 코드https://github.com/andygr/ml-regression
공식 코드https://github.com/andyisokay/delta-influence
공식 코드https://github.com/andyreagan/sentiment-analysis-comparison
공식 코드https://github.com/aniket004/bri3l
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.