https://github.com/flc777/glat
공식 코드Glancing Transformer for Non-Autoregressive Neural Machine Translation
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Glancing Transformer for Non-Autoregressive Neural Machine Translation
SMATCH++: Standardized and Extended Evaluation of Semantic Graphs
MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis
Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step
Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration
Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration
Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU
NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction
M-RewardBench: Evaluating Reward Models in Multilingual Settings
Cold-start Active Learning through Self-supervised Language Modeling
ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data
Ring Attention with Blockwise Transformers for Near-Infinite Context
Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Models
Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Models
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment
Discourse structure interacts with reference but not syntax in neural language models
Generative Region-Language Pretraining for Open-Ended Object Detection
BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation
Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankers
Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model
Dispatcher: A Message-Passing Approach To Language Modelling
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies
Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.