https://github.com/aksw/llm-kg-bench
공식 코드Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering
Papers with Code 아카이브
Dataset Generation
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The task involves enhancing the training of target application (e.g. autonomous driving systems) by generating datasets of diverse and critical elements (e.g. traffic scenarios). Traditional methods rely on expensive and limited datasets, which often fail to capture rare but essential situations that can pose risks during testing.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/aksw/llm-kg-bench-results
공식 코드Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering
https://github.com/andrewcaunes/ia4markings
공식 코드https://github.com/anindex/gtmp
공식 코드https://github.com/antoyang/just-ask
공식 코드https://github.com/anubhav1997/youneednodataset
공식 코드Zero-shot racially balanced dataset generation using an existing biased StyleGAN2
https://github.com/anwar-said/neurograph
공식 코드NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
https://github.com/appfl/cpbert
공식 코드Enhancing Clinical Models with Pseudo Data for De-identification
https://github.com/awslabs/graphrag-toolkit
공식 코드https://github.com/bastiengiraud/dsa-learn
공식 코드A Dataset Generation Toolbox for Dynamic Security Assessment: On the Role of the Security Boundary
https://github.com/bin123apple/autocoder
공식 코드AutoCoder: Enhancing Code Large Language Model with \textsc{AIEV-Instruct}
https://github.com/bnn-upc/graphlaxy
공식 코드Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation
https://github.com/braindatalab/gecobench
공식 코드GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations
https://github.com/c-chocolate/jabberwock
공식 코드https://github.com/c-juhwan/unigen
공식 코드https://github.com/carla-simulator/carla
공식 코드Synthetic Dataset Generation for Adversarial Machine Learning Research
https://github.com/ccp-eva/bonobosclassification
공식 코드Dataset Generation and Bonobo Classification from Weakly Labelled Videos
https://github.com/covisionlab/diffusion_labeling
공식 코드Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map
https://github.com/cschenxiang/uav-rain1k
공식 코드UAV-Rain1k: A Benchmark for Raindrop Removal from UAV Aerial Imagery
https://github.com/cysecbench/dataset
공식 코드https://github.com/danial-yazdani/ddg-matlab
공식 코드https://github.com/debarshikundupsu/scifaultyqa
공식 코드https://github.com/dilis-lab/simbaml_examples
공식 코드SimbaML: Connecting Mechanistic Models and Machine Learning with Augmented Data
https://github.com/dmis-lab/liquid
공식 코드LIQUID: A Framework for List Question Answering Dataset Generation
https://github.com/drlxj/camdiff
공식 코드https://github.com/eabdullin/optimouse-quest
공식 코드https://github.com/ericssonresearch/colabsfm
공식 코드ColabSfM: Collaborative Structure-from-Motion by Point Cloud Registration
https://github.com/ethanbradley/synfintabgen
공식 코드SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table Extraction
https://github.com/ethz-asl/camera_miscalib_detection
공식 코드https://github.com/explainableml/datadream
공식 코드https://github.com/explainableml/loft
공식 코드LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance
https://github.com/flairnlp/fabricator
공식 코드Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
https://github.com/flowfrontiers/cyberml-dataquality
공식 코드Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case Study
https://github.com/gamleksi/affordancegym
공식 코드https://github.com/gaussianer/fasterseg
공식 코드https://github.com/google-research-datasets/Synthetic-Persona-Chat
공식 코드Faithful Persona-based Conversational Dataset Generation with Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.