https://github.com/nju-websoft/OpenEA
공식 코드A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
Papers with Code 아카이브
Benchmarking
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nju-websoft/eventea
공식 코드EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs
https://github.com/nl2code/codes
공식 코드CodeS: Natural Language to Code Repository via Multi-Layer Sketch
https://github.com/nlie2/triage
공식 코드TRIAGE: Ethical Benchmarking of AI Models Through Mass Casualty Simulations
https://github.com/nliulab/mimic4ed-benchmark
공식 코드https://github.com/nlp-waseda/traveling-across-languages
공식 코드Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs
https://github.com/nlp2ct/detectrl
공식 코드DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios
https://github.com/nlpcode/tubench
공식 코드TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions
https://github.com/nlpforua/zno
공식 코드ZNO-Eval: Benchmarking reasoning capabilities of large language models in Ukrainian
https://github.com/noheadwuzonglin/mca-bench
공식 코드MCA-Bench: A Multimodal Benchmark for Evaluating CAPTCHA Robustness Against VLM-based Attacks
https://github.com/nojhan/IOHexperimenter
공식 코드Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks
https://github.com/nojhan/paradiseo
공식 코드Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks
https://github.com/nokiroki/recsys-evaluation-of-dozens-datasets
공식 코드From Variability to Stability: Advancing RecSys Benchmarking Practices
https://github.com/nomothings/charge
공식 코드Benchmarking Multimodal RAG through a Chart-based Document Question-Answering Generation Framework
https://github.com/novasearch-team/rag-retrieval
공식 코드Benchmarking the Myopic Trap: Positional Bias in Information Retrieval
https://github.com/novelqa/novelqa.github.io
공식 코드NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens
https://github.com/noy-sternlicht/debatable-intelligence
공식 코드Debatable Intelligence: Benchmarking LLM Judges via Debate Speech Evaluation
https://github.com/nrehush/minknet-tree-species
공식 코드https://github.com/ntaylorox/public_clinical_prompt
공식 코드https://github.com/nttmdlab-nlp/ToMATO
공식 코드ToMATO: Verbalizing the Mental States of Role-Playing LLMs for Benchmarking Theory of Mind
https://github.com/ntu-icg/fm-planner
공식 코드FM-Planner: Foundation Model Guided Path Planning for Autonomous Drone Navigation
https://github.com/nubagcilab/cirrmri600plus
공식 코드Large Scale MRI Collection and Segmentation of Cirrhotic Liver
https://github.com/nubagcilab/pansegnet
공식 코드Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
https://github.com/numbbo/coco
공식 코드COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
https://github.com/nuprl/multipl-e
공식 코드MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation
https://github.com/nvlabs/bongard-hoi
공식 코드Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions
https://github.com/nvlabs/nprach_synch
공식 코드https://github.com/nvlabs/radio
공식 코드AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One
https://github.com/nvlabs/verilog-eval
공식 코드VerilogEval: Evaluating Large Language Models for Verilog Code Generation
https://github.com/nyu-mlda/openabc
공식 코드OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit Synthesis
https://github.com/nyuad-cai/medarabiq
공식 코드MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks
https://github.com/odabashi/LMs-in-News-Summarization
공식 코드Unraveling the Capabilities of Language Models in News Summarization
https://github.com/oetu/otis
공식 코드Towards Generalisable Time Series Understanding Across Domains
https://github.com/ofa-sys/air-bench
공식 코드AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension
https://github.com/offdynamicsrl/off-dynamics-rl
공식 코드https://github.com/oleksost/latent_cl
공식 코드Continual Learning with Foundation Models: An Empirical Study of Latent Replay
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.