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

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

B

BERT Boosting Bias Backpropagation Batch Normalization

C

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

D

Deepfake Discriminative Model Data Augmentation Deep Learning Deterministic Model

E

Epoch Explainable AI (XAI)Embedding Encoder Ensemble Learning

F

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

G

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

H

Hierarchical Model Hallucination Heuristic Hyperparameter Hidden Layer

I

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

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

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

L

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

M

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

N

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

O

One-hot Encoding Optimizer Objective Function Online Learning Overfitting

P

Prompt Pooling Parameter Policy / Reinforcement Learning Policy Pretraining

Q

Q-learning Quality Estimation Query Quantization Queue / Buffer

R

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

S

Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

Tokenizer Transformer Training Data Tuning / Hyperparameter Tuning Transfer Learning

U

U-Net Uncertainty Estimation Underfitting Unsupervised Learning Universal Approximation Theorem

V

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

W

Weight Decay Workflow Weak Supervision Whitening / Whitening Transformation Word Embedding

X

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

Y

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

Z

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

什麼是 Instruction tuning

Instruction tuning 是機器學習和自然語言處理領域的一項技術，旨在調整模型以更好地理解和執行特定指令或任務。該過程通常在預訓練模型的基礎上進行，旨在提高模型在特定應用場景中的表現。

隨著人工智慧（AI）技術的快速發展，Instruction tuning 的重要性日益增強。它使大型語言模型（如 GPT 系列）能夠更有效地回應用戶需求，並提供準確且相關的結果。這項技術的成功實施將直接影響人機交互的自然性和效率。

Instruction tuning 通常涉及使用少量特定任務的數據進行微調，使模型在處理這些任務時更為有效。通過引入指令或示例，模型能夠更準確地理解上下文，並根據指令生成相應的輸出。

在問答系統、對話生成和文本摘要等應用中，Instruction tuning 能顯著提高模型的表現。例如，在醫療領域的智能助手中，經過 Instruction tuning 的模型能夠更準確地理解醫生的指令，並提供更相關的建議。

展望未來，Instruction tuning 可能會與更多自監督學習方法結合，以進一步提高模型的泛化能力。同時，隨著多個行業對個性化和定制化需求的增加，Instruction tuning 在多個領域中將發揮更大的作用。

Instruction tuning 的優點包括提高任務特定性能和用戶滿意度，而缺點可能涉及高數據需求和調優過程複雜等問題。此外，過度調優可能導致模型對特定任務的過擬合。

在進行 Instruction tuning 時，必須仔細選擇數據集，確保其代表性和多樣性，以避免模型在特定任務上的偏見。