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공식 코드CodeEditor: Learning to Edit Source Code with Pre-trained Models
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
CodeEditor: Learning to Edit Source Code with Pre-trained Models
Multi-Grained Patch Training for Efficient LLM-based Recommendation
Decoupled Sequence and Structure Generation for Realistic Antibody Design
Semantic-Based Self-Critical Training For Question Generation
Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across Modalities
LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language Model
LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT Plugins
Mixture of Tokens: Continuous MoE through Cross-Example Aggregation
Breaking the Silence: the Threats of Using LLMs in Software Engineering
Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving
Few-Shot Detection of Machine-Generated Text using Style Representations
BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models
Localized Vision-Language Matching for Open-vocabulary Object Detection
HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation
Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey
When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised
Dialogue-adaptive Language Model Pre-training From Quality Estimation
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Efficient Black-Box Adversarial Attacks on Neural Text Detectors
Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.