https://github.com/INK-USC/VG-CCL
공식 코드Visually Grounded Continual Learning of Compositional Phrases
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Visually Grounded Continual Learning of Compositional Phrases
Visually Grounded Continual Learning of Compositional Phrases
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection
Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling
LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models
BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies
MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus Infection
Watermark under Fire: A Robustness Evaluation of LLM Watermarking
Low-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline
Benchmarking Language Model Creativity: A Case Study on Code Generation
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language Model
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding
MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech Tokens
Large Language Model Recall Uncertainty is Modulated by the Fan Effect
BERT Loses Patience: Fast and Robust Inference with Early Exit
Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classification
AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
Why gradient clipping accelerates training: A theoretical justification for adaptivity
Explaining Context Length Scaling and Bounds for Language Models
Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response Theory
Scrambled text: training Language Models to correct OCR errors using synthetic data
Scrambled text: training Language Models to correct OCR errors using synthetic data
Scrambled text: training Language Models to correct OCR errors using synthetic data
Guiding In-Context Learning of LLMs through Quality Estimation for Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.