https://github.com/YouzhiTian/Structured-Content-Preservation-for-Unsupervised-Text-Style-Transfer
공식 코드Structured Content Preservation for Unsupervised Text Style Transfer
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Structured Content Preservation for Unsupervised Text Style Transfer
ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis
Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
Emulated Disalignment: Safety Alignment for Large Language Models May Backfire!
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization
CMD: a framework for Context-aware Model self-Detoxification
MotionLLaMA: A Unified Framework for Motion Synthesis and Comprehension
ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization
DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
Instance Similarity Learning for Unsupervised Feature Representation
500xCompressor: Generalized Prompt Compression for Large Language Models
ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction
Distilling Coarse-to-Fine Semantic Matching Knowledge for Weakly Supervised 3D Visual Grounding
Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs
Training-free Neural Architecture Search for RNNs and Transformers
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients
Punctuation Restoration Improves Structure Understanding Without Supervision
InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.