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Glossary

0-9
0-shot learning1-shot learning2-stage detector3D convolution4D data5G + AI6DoF pose estimation7D representation8-bit quantization9-layer network
A
AGI / Artificial General IntelligenceAlgorithmArtificial Intelligence (AI)AttentionAutoencoder
B
BackpropagationBatch NormalizationBERTBiasBoosting
C
ChatbotClassifier / ClassificationClusteringCNN / Convolutional Neural NetworkCross-Validation
D
Data AugmentationDeep LearningDeepfakeDeterministic ModelDiscriminative Model
E
EmbeddingEncoderEnsemble LearningEpochExplainable AI (XAI)
F
Feature ExtractionFine-tuningForward PropagationFoundation ModelFusion / Multimodal Fusion
G
GAN / Generative Adversarial NetworkGenerative AIGradient DescentGraph Neural Network (GNN)Grounding
H
HallucinationHeuristicHidden LayerHierarchical ModelHyperparameter
I
Imbalanced DataInstance / SampleInstruction tuningIntelligence Amplification / AugmentationInterpretability
J
JAXJitteringJoint EmbeddingJSONL / JSON-linesJuxtaposition
K
K-means ClusteringK-Shot LearningKernel TrickKL Divergence (Kullback–Leibler Divergence)Knowledge Distillation
L
Large Language Model (LLM)Latent VariableLearning RateLoss FunctionLSTM / Long Short-Term Memory
M
Machine Learning (ML)Meta-learningModelMulti-head AttentionMultimodal / Multimodality
N
Neural NetworkNLP / Natural Language ProcessingNLU / Natural Language UnderstandingNormalizationNovelty Detection / Anomaly Detection
O
Objective FunctionOne-hot EncodingOnline LearningOptimizerOverfitting
P
ParameterPolicy / Reinforcement Learning PolicyPoolingPretrainingPrompt
Q
Q-learningQuality EstimationQuantizationQueryQueue / Buffer
R
RegularizationReinforcement Learning (RL)Representation LearningRetrieval Augmented Generation (RAG)RNN / Recurrent Neural Network
S
SamplingSelf-Supervised LearningSequence ModelingSoftmaxSupervised Learning
T
TokenizerTraining DataTransfer LearningTransformerTuning / Hyperparameter Tuning
U
U-NetUncertainty EstimationUnderfittingUniversal Approximation TheoremUnsupervised Learning
V
Validation SetVanishing / Exploding GradientVariational Autoencoder (VAE)Vector EmbeddingVision Transformer (ViT)
W
Weak SupervisionWeight DecayWhitening / Whitening TransformationWord EmbeddingWorkflow
X
X-axis / feature axisXAI / Explainable AIXLMXLNetXOR problem
Y
Y-axis / feature axisY-transform / YUVYAGNI (You Aren't Gonna Need It)Yield (model yield / throughput)Yoga of AI
Z
Z-score NormalizationZero-centric / Zero-bias initializationZero-gradient phenomenonZero-shot Learning / Zero-shot inferenceZygosity in augmentation

What is Instruction tuning

Instruction tuning is a technique in the fields of machine learning and natural language processing aimed at adjusting models to better understand and execute specific instructions or tasks. This process typically occurs on the foundation of pre-trained models, with the goal of enhancing the model's performance in particular application scenarios.


The significance of instruction tuning has grown with the rapid advancement of artificial intelligence (AI) technologies. It enables large language models, like the GPT series, to respond more effectively to user needs and deliver accurate and relevant results. The successful implementation of this technique will directly impact the naturalness and efficiency of human-machine interaction.


Instruction tuning usually involves fine-tuning a model using a small amount of data specific to a task, allowing it to perform more effectively in handling those tasks. By introducing instructions or examples, the model can better grasp the context and generate appropriate outputs based on the directives.


In applications such as question-answering systems, dialogue generation, and text summarization, instruction tuning can significantly improve model performance. For instance, in the medical field's intelligent assistants, models that have undergone instruction tuning can more accurately understand physician directives and offer more relevant recommendations.


Looking ahead, instruction tuning may integrate with more self-supervised learning methods to further enhance model generalization capabilities. Additionally, as more industries demand personalization and customization, instruction tuning is poised to play a larger role across multiple sectors.


While the advantages of instruction tuning include improved task-specific performance and user satisfaction, potential downsides may involve high data requirements and complex tuning processes. Furthermore, excessive tuning may lead to model overfitting on specific tasks.


Careful selection of datasets is crucial when conducting instruction tuning to ensure representativeness and diversity, thereby avoiding biases in the model's task performance.