https://github.com/hitum-dev/pollmgraph
공식 코드PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics
Papers with Code 아카이브
Data-free Knowledge Distillation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hiyouga/llama-factory
공식 코드CodeS: Natural Language to Code Repository via Multi-Layer Sketch
https://github.com/hiyuchang/leadq
공식 코드Learn How to Query from Unlabeled Data Streams in Federated Learning
https://github.com/hj-n/labeled-datasets
공식 코드Sanity Check for External Clustering Validation Benchmarks using Internal Validation Measures
https://github.com/hjytsuki/mldebugging
공식 코드MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios
https://github.com/hku-system-security-lab/congra
공식 코드https://github.com/hkunlp/progen
공식 코드ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
https://github.com/hkust-knowcomp/abspyramid
공식 코드AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph
https://github.com/hkust-knowcomp/cskb-population
공식 코드Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset
https://github.com/hkust-knowcomp/efo-1-qa-benchmark
공식 코드Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs
https://github.com/hkust-knowcomp/mars
공식 코드https://github.com/hkust-longgroup/awesome-mllm-benchmarks
공식 코드A Survey on Multimodal Benchmarks: In the Era of Large AI Models
https://github.com/hkust-nlp/agentboard
공식 코드AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents
https://github.com/hkust-nlp/felm
공식 코드FELM: Benchmarking Factuality Evaluation of Large Language Models
https://github.com/hkustdial/deepfund
공식 코드Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarking
https://github.com/hlt-mt/fbk-neutr-eval
공식 코드Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus
https://github.com/hlt-mt/speech-massive
공식 코드Speech-MASSIVE: A Multilingual Speech Dataset for SLU and Beyond
https://github.com/holistic-ai/SAGED-Bias
공식 코드https://github.com/holistic-ai/THaMES
공식 코드THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models
https://github.com/horsepurve/StyleSegor
공식 코드Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
https://github.com/howiehwong/awareness-in-llm
공식 코드I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench
https://github.com/howiehwong/unigen
공식 코드UniGen: A Unified Framework for Textual Dataset Generation Using Large Language Models
https://github.com/hpai-bsc/prompt_engine
공식 코드https://github.com/hpnair/18663_Project_FBNet
공식 코드https://github.com/hse-lambda/digital-twin
공식 코드Performance Modeling of Data Storage Systems using Generative Models
https://github.com/hsouri/battle-of-the-backbones
공식 코드Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
https://github.com/hsp-iit/prosthetic-grasping-simulation
공식 코드Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis
https://github.com/htqin/bibench
공식 코드https://github.com/huangd1999/EffiBench
공식 코드EffiBench: Benchmarking the Efficiency of Automatically Generated Code
https://github.com/huawei-noah/SMARTS
공식 코드https://github.com/huawei-noah/hebo
공식 코드AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
https://github.com/hugcis/benchmark_learning_efficiency
공식 코드Benchmarking Learning Efficiency in Deep Reservoir Computing
https://github.com/hugcis/incremental_tasks
공식 코드Benchmarking Learning Efficiency in Deep Reservoir Computing
https://github.com/hugozhl/hetu
공식 코드Experimental Analysis of Large-scale Learnable Vector Storage Compression
https://github.com/hulitaotom/Joint-Multi-Scale-Tone-Mapping-and-Denoising-for-HDR-Image-Enhancement
공식 코드Joint Multi-Scale Tone Mapping and Denoising for HDR Image Enhancement
https://github.com/humza909/llm_survey
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.