https://github.com/METR/ai-rd-tasks
공식 코드RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
Papers with Code 아카이브
Language Modeling
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MIR-MU/pine
공식 코드https://github.com/MJinXiang/Reasoning-Table
공식 코드Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
https://github.com/ML-KULeuven/deepstochlog-lm
공식 코드Valid Text-to-SQL Generation with Unification-based DeepStochLog
https://github.com/MYC000801/SE-POPO
공식 코드Avoiding $\mathbf{exp(R_{max})}$ scaling in RLHF through Preference-based Exploration
https://github.com/MaLA-LM/emma-500
공식 코드EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models
https://github.com/MaastrichtU-IDS/LMKBC-2023
공식 코드Expanding the Vocabulary of BERT for Knowledge Base Construction
https://github.com/Machine-Learning-Tokyo/Poetry-GAN
공식 코드https://github.com/Maddy12/MultiModalVideoRobustness/tree/master/code
공식 코드Robustness Analysis of Video-Language Models Against Visual and Language Perturbations
https://github.com/MadryLab/DsDm
공식 코드https://github.com/MadryLab/platinum-benchmarks
공식 코드https://github.com/Maniyantingliu/generation_with_dynamic_vocabulary
공식 코드https://github.com/Mao-KU/lightweight-crosslingual-sent2vec
공식 코드https://github.com/MarcoMeter/neroRL
공식 코드https://github.com/MarwahAlaofi/SIGIR-23-SRP-UQV100-GPT-Query-Variants
공식 코드Can Generative LLMs Create Query Variants for Test Collections? An Exploratory Study
https://github.com/MasterAI-EAM/MatEmbedding
공식 코드From Tokens to Materials: Leveraging Language Models for Scientific Discovery
https://github.com/Masum06/Text2App
공식 코드Text2App: A Framework for Creating Android Apps from Text Descriptions
https://github.com/Mathugo/AAAI2025-CarFormer-EPredictor
공식 코드Harnessing Event Sensory Data for Error Pattern Prediction in Vehicles: A Language Model Approach
https://github.com/Max-Fu/tvl
공식 코드A Touch, Vision, and Language Dataset for Multimodal Alignment
https://github.com/Mayer123/HyKAS-CSKG
공식 코드Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering
https://github.com/MedHK23/MedViLaM
공식 코드https://github.com/Mehrad0711/HUBERT
공식 코드https://github.com/Mhzzzzz/ADG-steganography
공식 코드https://github.com/Microsoft/PTVS
공식 코드https://github.com/MikeGu721/DetectBench
공식 코드DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?
https://github.com/Milozms/TSMH
공식 코드https://github.com/MingliangLiang3/WFPP
공식 코드Enhancing Vision-Language Model Pre-training with Image-text Pair Pruning Based on Word Frequency
https://github.com/MinkyuKim26/Prefix_AAC_ICASSP2023
공식 코드https://github.com/Miracle1207/Mean-Field-LLM
공식 코드MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework
https://github.com/MiuLab/Lattice-ELMo
공식 코드Learning Spoken Language Representations with Neural Lattice Language Modeling
https://github.com/MiuLab/Lattice-SLU
공식 코드Learning Spoken Language Representations with Neural Lattice Language Modeling
https://github.com/MiuLab/PE-Study
공식 코드https://github.com/MiuLab/RCT-Gen
공식 코드Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation
https://github.com/Mlair77/nmt_adequacy
공식 코드Prevent the Language Model from being Overconfident in Neural Machine Translation
https://github.com/Moe-Zbeeb/Optimizing-Deep-Neural-Networks-via-Safety-Guided-Self-Compression
공식 코드Optimizing Deep Neural Networks using Safety-Guided Self Compression
https://github.com/MohammadrezaBanaei/orientation_based_embedding_compression
공식 코드Direction is what you need: Improving Word Embedding Compression in Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.