https://github.com/763337092/LP-BERT
공식 코드Multi-task Pre-training Language Model for Semantic Network Completion
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Multi-task Pre-training Language Model for Semantic Network Completion
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation
Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks
ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area
Steering Away from Harm: An Adaptive Approach to Defending Vision Language Model Against Jailbreaks
TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control
AdapterFusion: Non-Destructive Task Composition for Transfer Learning
Deeper Text Understanding for IR with Contextual Neural Language Modeling
Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning
InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists
PROP: Pre-training with Representative Words Prediction for Ad-hoc Retrieval
Language Model Tokenizers Introduce Unfairness Between Languages
MAGMA -- Multimodal Augmentation of Generative Models through Adapter-based Finetuning
ColBERT Retrieval and Ensemble Response Scoring for Language Model Question Answering
ComStreamClust: a communicative multi-agent approach to text clustering in streaming data
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
Vision-Language Pre-Training for Boosting Scene Text Detectors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.