Techniques on English AI Terms Dictionary

Reflection

Sat, 18 Jul 2026 10:13:50 +0000

Definition

In AI, reflection is a paradigm where a model pauses to evaluate its own generation process or output before finalizing it. This can involve checking for logical consistency, factual accuracy, or adherence to safety guidelines. By reflecting on its own actions, the system can correct errors, refine arguments, or adjust its tone. This technique is often implemented via chain-of-thought prompting or separate critique models, significantly enhancing the reliability and quality of complex reasoning tasks.

Hyperparameter Tuning

Sat, 18 Jul 2026 10:01:39 +0000

Definition

Hyperparameter tuning involves evaluating different sets of hyperparameters to find the configuration that yields the best model accuracy or lowest error rate. Common strategies include grid search, which exhaustively checks all combinations, and random search, which samples randomly. More advanced techniques use Bayesian optimization to intelligently select promising configurations based on previous results. This process is computationally expensive but essential for maximizing the potential of machine learning models.

Summary

The process of systematically searching for the best combination of hyperparameters to optimize model performance.

Grounding

Sat, 18 Jul 2026 10:00:30 +0000

Definition

In AI, grounding refers to linking symbolic representations or generated text to concrete real-world entities, data, or sensory experiences. For language models, this often involves Retrieval-Augmented Generation (RAG), where the model retrieves factual information from external databases to ground its responses in verified data rather than relying solely on internal weights. In robotics, grounding connects language commands to physical actions or sensor inputs, ensuring the AI understands the context of its environment.

training-free

Sat, 18 Jul 2026 09:39:43 +0000

Definition

Training-free approaches refer to techniques that modify model behavior or output without updating the underlying weights via backpropagation. These methods often leverage prompt engineering, feature manipulation, or external knowledge retrieval to improve performance on specific tasks. They are valuable for reducing computational costs and avoiding catastrophic forgetting, allowing rapid adaptation to new domains using pre-trained models directly.

Summary

Methods that adapt or enhance models without performing gradient-based parameter updates.

Combining

Sat, 18 Jul 2026 09:30:47 +0000

Definition

This concept encompasses methods like ensemble learning, where predictions from several models are aggregated to reduce variance or bias. It also includes multimodal fusion, where different types of data such as text and images are combined to create richer representations. By leveraging diverse inputs or algorithms, combining strategies often yield more accurate and reliable results than single-model approaches.

Summary

Combining in AI refers to the integration of multiple models, data sources, or techniques to improve overall performance and robustness.