https://github.com/kimiyoung/transformer-xl
공식 코드Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling
Telling Stories for Common Sense Zero-Shot Action Recognition
Interpreting Large Text-to-Image Diffusion Models with Dictionary Learning
SpaceByte: Towards Deleting Tokenization from Large Language Modeling
Improving SSVEP BCI Spellers With Data Augmentation and Language Models
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling
Class-relation Knowledge Distillation for Novel Class Discovery
Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations
GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
OntoChatGPT Information System: Ontology-Driven Structured Prompts for ChatGPT Meta-Learning
ImaginaryNet: Learning Object Detectors without Real Images and Annotations
Q-learning with Language Model for Edit-based Unsupervised Summarization
Grounding Language Models to Images for Multimodal Inputs and Outputs
MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks
FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models
Efficient Pre-training of Masked Language Model via Concept-based Curriculum Masking
Studying Large Language Model Behaviors Under Context-Memory Conflicts With Real Documents
Compositional Language Understanding with Text-based Relational Reasoning
SMART: Submodular Data Mixture Strategy for Instruction Tuning
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Characterizing Verbatim Short-Term Memory in Neural Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.