https://github.com/thu-coai/augesc
공식 코드AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation
MiniPLM: Knowledge Distillation for Pre-Training Language Models
Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization
FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant
RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style
Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment
Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control
Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models
Noise Contrastive Alignment of Language Models with Explicit Rewards
MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression
AutoWebGLM: A Large Language Model-based Web Navigating Agent
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers
GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
Extensive Self-Contrast Enables Feedback-Free Language Model Alignment
Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
CB-Conformer: Contextual biasing Conformer for biased word recognition
A Clustering Framework for Unsupervised and Semi-supervised New Intent Discovery
AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
GLaPE: Gold Label-agnostic Prompt Evaluation and Optimization for Large Language Model
ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation
Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.