#training

機械支援の翻訳下書き (Japanese) for "Fine-Tuning": The process of further training a pre-trained model on a smaller, task-specific dataset to adapt its behavior for a particular domain or style. Fine-tuning updates the model's weights to make it perform better on specific tasks without training from scratch.

“例文の下書き: We fine-tuned the base model on our legal contracts corpus so it could draft clauses in the right style.”

機械支援の翻訳下書き (Japanese) for "RLHF": Reinforcement Learning from Human Feedback — a training technique used to align language models with human preferences. Human raters compare model outputs and choose the better response; these preferences train a reward model which then guides further fine-tuning via reinforcement learning.

“例文の下書き: RLHF is the key step that turns a raw language model into a helpful, harmless assistant.”

機械支援の翻訳下書き (Japanese) for "Synthetic Data": Artificially generated data that mimics the statistical properties of real-world data, used for training or testing AI models. Synthetic data can be created by generative models, rule-based systems, or simulations, and is especially valuable when real data is scarce, sensitive, or expensive to collect.

“例文の下書き: We generated synthetic medical records to train the model without risking patient privacy.”