Artificially generated training data produced by LLMs or other AI models, used to augment or replace human-annotated datasets. Techniques include prompt-based generation, retrieval-augmented pipelines, and iterative self-refinement. Synthetic data slashes costs from $5-20 per human preference point to under $0.01 per sample and became central to post-training pipelines in 2024-2025.
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Artificially generated training data produced by LLMs or other AI models, used to augment or replace human-annotated datasets. Techniques include prompt-based generation, retrieval-augmented pipelines, and iterative self-refinement. Synthetic data slashes costs from $5-20 per human preference point to under $0.01 per sample and became central to post-training pipelines in 2024-2025.
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Synthetic Data (AI Training) (synthetic-data) Category: AI / ML Definition: Artificially generated training data produced by LLMs or other AI models, used to augment or replace human-annotated datasets. Techniques include prompt-based generation, retrieval-augmented pipelines, and iterative self-refinement. Synthetic data slashes costs from $5-20 per human preference point to under $0.01 per sample and became central to post-training pipelines in 2024-2025. Aliases: AI-Generated Training Data Related: Knowledge Distillation, DPO (Direct Preference Optimization), Fine-Tuning
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A technique for transferring capabilities from a large 'teacher' model to a smaller 'student' model, typically by having the teacher generate a synthetic dataset that the student is fine-tuned on. Distilled models can match or exceed teacher performance on specific tasks while being much cheaper to deploy. Common in 2024-2025 for creating efficient specialized models.
A simplified alternative to RLHF that aligns LLM outputs with human preferences without training a separate reward model or using reinforcement learning. DPO directly optimizes a policy using pairs of preferred and dispreferred outputs, making it computationally cheaper and more stable than RLHF's multi-stage pipeline. Widely adopted in 2024-2025 for fine-tuning open-source models.
The process of further training a pre-trained model on a specialized dataset to improve performance on specific tasks. Fine-tuning adapts a foundation model's weights using domain-specific data (e.g., Solana documentation, smart contract code). Techniques include full fine-tuning, LoRA (Low-Rank Adaptation), and QLoRA. Fine-tuned models can outperform general models on narrow tasks.
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The process of optimizing a model's parameters by exposing it to data and adjusting weights to minimize a loss function. Pre-training on large datasets creates foundation models. Training LLMs requires massive compute (thousands of GPUs, weeks/months). Training data quality, diversity, and size directly impact model capabilities. Distinguished from fine-tuning (smaller scale, specific domain).
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A technique for transferring capabilities from a large 'teacher' model to a smaller 'student' model, typically by having the teacher generate a synthetic dataset that the student is fine-tuned on. Distilled models can match or exceed teacher performance on specific tasks while being much cheaper to deploy. Common in 2024-2025 for creating efficient specialized models.
A simplified alternative to RLHF that aligns LLM outputs with human preferences without training a separate reward model or using reinforcement learning. DPO directly optimizes a policy using pairs of preferred and dispreferred outputs, making it computationally cheaper and more stable than RLHF's multi-stage pipeline. Widely adopted in 2024-2025 for fine-tuning open-source models.
The process of further training a pre-trained model on a specialized dataset to improve performance on specific tasks. Fine-tuning adapts a foundation model's weights using domain-specific data (e.g., Solana documentation, smart contract code). Techniques include full fine-tuning, LoRA (Low-Rank Adaptation), and QLoRA. Fine-tuned models can outperform general models on narrow tasks.
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A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.
The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).
A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.
A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.