https://github.com/mesotron/teaching_transformers
공식 코드Teaching Autoregressive Language Models Complex Tasks By Demonstration
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Teaching Autoregressive Language Models Complex Tasks By Demonstration
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual Similarity
PerSRV: Personalized Sticker Retrieval with Vision-Language Model
Bridging the Gap between Structural and Semantic Similarity in Diverse Planning
E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple Prediction
SeFNet: Bridging Tabular Datasets with Semantic Feature Nets
Mapping Memes to Words for Multimodal Hateful Meme Classification
PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain
Modeling Disclosive Transparency in NLP Application Descriptions
VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models
ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptation
LM-Critic: Language Models for Unsupervised Grammatical Error Correction
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding
GraphCodeBERT: Pre-training Code Representations with Data Flow
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL
Fusing Context Into Knowledge Graph for Commonsense Question Answering
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference
GLIPv2: Unifying Localization and Vision-Language Understanding
GIT: A Generative Image-to-text Transformer for Vision and Language
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text
LLM2CLIP: Powerful Language Model Unlocks Richer Visual Representation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.