https://github.com/NohTow/PPL-MCTS
공식 코드PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding
Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP Standards
u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model
Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning
Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript
Tool-Planner: Task Planning with Clusters across Multiple Tools
Large Language Model-Driven Curriculum Design for Mobile Networks
AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With
MobA: Multifaceted Memory-Enhanced Adaptive Planning for Efficient Mobile Task Automation
IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generation
MotionChain: Conversational Motion Controllers via Multimodal Prompts
PoET: A generative model of protein families as sequences-of-sequences
A Comparison of Adaptation Techniques and Recurrent Neural Network Architectures
HMT: Hierarchical Memory Transformer for Long Context Language Processing
REBEL: Reinforcement Learning via Regressing Relative Rewards
Drive Like a Human: Rethinking Autonomous Driving with Large Language Models
Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability
Reinforcement Learning Friendly Vision-Language Model for Minecraft
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
Enhancing RWKV-based Language Models for Long-Sequence Text Generation
PP-DocBee: Improving Multimodal Document Understanding Through a Bag of Tricks
ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages
ERNIE: Enhanced Representation through Knowledge Integration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.