https://github.com/MI2DataLab/nlp_interpretability_framework
공식 코드Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems
Papers with Code 아카이브
Language Modelling
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MICV-yonsei/EAGLE
공식 코드EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
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/MeLL-UFF/tuning_sentiment
공식 코드https://github.com/MedHK23/MedViLaM
공식 코드https://github.com/Mehrad0711/HUBERT
공식 코드https://github.com/Mercidaiha/IRT-Router
공식 코드IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory
https://github.com/Mhzzzzz/ADG-steganography
공식 코드https://github.com/MichiganNLP/video-fill-in-the-blank
공식 코드FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework
https://github.com/Microsoft/EdgeML
공식 코드FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
https://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/Mikexu007/PCRP
공식 코드Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition
https://github.com/Milozms/TSMH
공식 코드https://github.com/MingLunHan/CIF-PyTorch
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.