https://github.com/yangcong356/kcfi
공식 코드Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning
BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Framework
Re3: Generating Longer Stories With Recursive Reprompting and Revision
Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs
ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues
Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts
SOLO: A Single Transformer for Scalable Vision-Language Modeling
Large Language Model Can Interpret Latent Space of Sequential Recommender
Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations
Fast Multipole Attention: A Divide-and-Conquer Attention Mechanism for Long Sequences
AF Adapter: Continual Pretraining for Building Chinese Biomedical Language Model
HOP: History-and-Order Aware Pre-training for Vision-and-Language Navigation
Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems
Strings from the Library of Babel: Random Sampling as a Strong Baseline for Prompt Optimisation
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Housekeep: Tidying Virtual Households using Commonsense Reasoning
Tree Transformer: Integrating Tree Structures into Self-Attention
CodeGRU: Context-aware Deep Learning with Gated Recurrent Unit for Source Code Modeling
CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling
Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt Verbalizer
On the Role of Attention Heads in Large Language Model Safety
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.