https://github.com/moe-zbeeb/exploring-the-landscape-for-generative-models-for-specialized-data-generation
공식 코드Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis
GSCLIP : A Framework for Explaining Distribution Shifts in Natural Language
An EcoSage Assistant: Towards Building A Multimodal Plant Care Dialogue Assistant
Repairing Language Model Pipelines by Meta Self-Refining Competing Constraints at Runtime
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models
A Linguistic Comparison between Human and ChatGPT-Generated Conversations
Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning
MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining
QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-Tuning
Optimizing Retrieval-augmented Reader Models via Token Elimination
Can ChatGPT's Responses Boost Traditional Natural Language Processing?
An Efficient Self-Supervised Cross-View Training For Sentence Embedding
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling
Controllable Text Generation with Neurally-Decomposed Oracle
JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its Applications
Synthesizing Interpretable Control Policies through Large Language Model Guided Search
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.