https://github.com/yxuansu/contrastive_search_is_what_you_need
공식 코드Contrastive Search Is What You Need For Neural Text Generation
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Contrastive Search Is What You Need For Neural Text Generation
Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations
FinBERT: A Pretrained Language Model for Financial Communications
Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations
ARWKV: Pretrain is not what we need, an RNN-Attention-Based Language Model Born from Transformer
BP-Transformer: Modelling Long-Range Context via Binary Partitioning
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts
Open-Vocabulary Argument Role Prediction for Event Extraction
MMNeuron: Discovering Neuron-Level Domain-Specific Interpretation in Multimodal Large Language Model
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model
Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings
ShizishanGPT: An Agricultural Large Language Model Integrating Tools and Resources
Measuring Social Biases in Masked Language Models by Proxy of Prediction Quality
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?
Deductive Additivity for Planning of Natural Language Proofs
Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous Driving
Planning with Multi-Constraints via Collaborative Language Agents
Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment
Towards Unifying Reference Expression Generation and Comprehension
Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy
InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains
ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge
Zero-Shot Cross-Lingual Transfer in Legal Domain Using Transformer Models
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.