https://github.com/ShuaiBai623/AIC2021-T5-CLV
공식 코드Connecting Language and Vision for Natural Language-Based Vehicle Retrieval
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Connecting Language and Vision for Natural Language-Based Vehicle Retrieval
LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning
Verdi: Quality Estimation and Error Detection for Bilingual Corpora
AttViz: Online exploration of self-attention for transparent neural language modeling
AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators
ViLP: Knowledge Exploration using Vision, Language, and Pose Embeddings for Video Action Recognition
SLIDE: Integrating Speech Language Model with LLM for Spontaneous Spoken Dialogue Generation
Squeezed Attention: Accelerating Long Context Length LLM Inference
SUGARCREPE++ Dataset: Vision-Language Model Sensitivity to Semantic and Lexical Alterations
Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections
Evaluating and Optimizing Educational Content with Large Language Model Judgments
Contextualized Semantic Distance between Highly Overlapped Texts
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASR
Hierarchical Quantized Representations for Script Generation
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
Controllable Generation from Pre-trained Language Models via Inverse Prompting
OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.