Glossary

0-9

1-shot learning 2-stage detector 3D convolution 4D data 5G + AI 6DoF pose estimation 7D representation 8-bit quantization 9-layer network 0-shot learning

A

Artificial Intelligence (AI)Algorithm Attention Autoencoder AGI / Artificial General Intelligence

B

BERT Boosting Backpropagation Batch Normalization Bias

C

Classifier / Classification Cross-Validation Clustering CNN / Convolutional Neural Network Chatbot

D

Discriminative Model Deterministic Model Deep Learning Data Augmentation Deepfake

E

Epoch Encoder Ensemble Learning Explainable AI (XAI)Embedding

F

Foundation Model Forward Propagation Fusion / Multimodal Fusion Feature Extraction Fine-tuning

G

GAN / Generative Adversarial Network Grounding Generative AI Gradient Descent Graph Neural Network (GNN)

H

Hierarchical Model Hyperparameter Hallucination Heuristic Hidden Layer

I

Interpretability Instruction tuning Imbalanced Data Instance / Sample Intelligence Amplification / Augmentation

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

K-Shot Learning Kernel Trick KL Divergence (Kullback–Leibler Divergence)Knowledge Distillation K-means Clustering

L

LSTM / Long Short-Term Memory Large Language Model (LLM)Latent Variable Loss Function Learning Rate

M

Multimodal / Multimodality Multi-head Attention Machine Learning (ML)Model Meta-learning

N

NLU / Natural Language Understanding Normalization Neural Network Novelty Detection / Anomaly Detection NLP / Natural Language Processing

O

One-hot Encoding Overfitting Objective Function Optimizer Online Learning

P

Prompt Parameter Pretraining Policy / Reinforcement Learning Policy Pooling

Q

Q-learning Query Queue / Buffer Quantization Quality Estimation

R

RNN / Recurrent Neural Network Representation Learning Retrieval Augmented Generation (RAG)Reinforcement Learning (RL)Regularization

S

Softmax Sampling Supervised Learning Sequence Modeling Self-Supervised Learning

T

Tokenizer Transformer Tuning / Hyperparameter Tuning Transfer Learning Training Data

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

Vision Transformer (ViT)Variational Autoencoder (VAE)Vector Embedding Vanishing / Exploding Gradient Validation Set

W

Weight Decay Word Embedding Workflow Whitening / Whitening Transformation Weak Supervision

X

X-axis / feature axis XLM XLNet XAI / Explainable AI XOR problem

Y

Yoga of AI Y-transform / YUV Yield (model yield / throughput)Y-axis / feature axis YAGNI (You Aren't Gonna Need It)

Z

Z-score Normalization Zero-gradient phenomenon Zero-shot Learning / Zero-shot inference Zero-centric / Zero-bias initialization Zygosity in augmentation

什么是 Instruction tuning

Instruction tuning 是机器学习和自然语言处理领域的一项技术，旨在调整模型以更好地理解和执行特定指令或任务。该过程通常在预训练模型的基础上进行，旨在提高模型在特定应用场景中的表现。

随着人工智能（AI）技术的快速发展，Instruction tuning 的重要性日益增强。它使大型语言模型（如 GPT 系列）能够更有效地响应用户需求，并提供准确且相关的结果。这项技术的成功实施将直接影响人机交互的自然性和效率。

Instruction tuning 通常涉及使用少量特定任务的数据进行微调，使模型在处理这些任务时更为有效。通过引入指令或示例，模型能够更准确地理解上下文，并根据指令生成相应的输出。

在问答系统、对话生成和文本摘要等应用中，Instruction tuning 能显著提高模型的表现。例如，在医疗领域的智能助手中，经过 Instruction tuning 的模型能够更准确地理解医生的指令，并提供更相关的建议。

展望未来，Instruction tuning 可能会与更多自监督学习方法结合，以进一步提高模型的泛化能力。同时，随着多个行业对个性化和定制化需求的增加，Instruction tuning 在多个领域中将发挥更大的作用。

Instruction tuning 的优点包括提高任务特定性能和用户满意度，而缺点可能涉及高数据需求和调优过程复杂等问题。此外，过度调优可能导致模型对特定任务的过拟合。

在进行 Instruction tuning 时，必须仔细选择数据集，确保其代表性和多样性，以避免模型在特定任务上的偏见。