| Theory | URL | Description |
|---|---|---|
| Collaborative Intelligence | Collaborative Intelligence | Combining the outputs of various models through a structured process of proposals and aggregations to enhance performance. |
| Iterative Refinement | Iterative Refinement | Each layer of LLM agents refines the outputs from the previous layer to improve the overall quality. |
| Specialization Limitation | Specialization Limitation | Individual models excel in specific tasks but struggle with others, necessitating the combination of multiple models. |
| Soft Splits in Decision Trees | Soft Splits in Decision Trees | Traditional decision trees create rigid structures, while soft splits allow inputs to traverse multiple paths with certain probabilities. |
| Low-Rank Decomposition Methods | Low-Rank Decomposition Methods | Techniques for model compression that create compact models with fewer parameters, enhancing efficiency. |
| Active Sampling | Active Sampling | A data selection method designed to choose the most representative portion of a dataset for a specific task. |
| Bounded Rationality | Bounded Rationality | Limitations in computational capacity and working memory constrain decision-making processes. |
| Retrieval Augmented Generation (RAG) | Retrieval Augmented Generation (RAG) | Combining retrieval and generation methods to improve performance without extensive retraining. |
Created
July 29, 2024 20:43
-
-
Save pydemo/2dad2fce9c3672dd436240e5e1b0c4c1 to your computer and use it in GitHub Desktop.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment