license:lgpl-3.0 region:us
---
license: lgpl-3.0
---
# tree-of-knowledge-llm
ToK aka Tree of Knowledge for Large Language Models LLM. It's a novel dataset that inspires knowledge symbolic correlation in simple input and output prompts.

https://github.com/fblgit/tree-of-knowledge-llm

The set experimentially can be used with multiple purposes:

* Knowledge Extraction from a Model
* Fine Tuning a model with newer data
* Create Granular Domain Knowledge Sets
* Improve training performance

Syntax Example:

```
  {
    "instruction": "Describe energy",
    "input": "",
    "output": "Energy AS ability TO do work OR cause change WITHIN system && forms of energy==[kinetic, potential, thermal, chemical, ... [TYPES]] && conservation of energy==law STATING energy CANNOT be created OR destroyed ONLY transformed BETWEEN forms && energy sources==[fossil fuels, nuclear, solar, wind, ... [
EXAMPLES]] USED TO power human activities"
  }
```
## Characteristics
* Introduces condensation masking with `...`
* Introduces hints keywords. example: `[ADVANTAGES]`, `[CHARACTERISTICS]`, `[RULES]`.
* Introduces directional keywords. example: `AS`, `AND`, `IN`, `BETWEEN`, `RANGING`.
* Introduces approach keywords. example: `NOTATED`, `PREDICTING`, `CALCULATED`
* Introduces efficient aggrupations keyword `===`
* Introduces separated relationship keyword `&&`

## Changelog
- 2023-05-20 - Released the first version of the dataset, illustrative examples.
- 2023-05-21 - Added the first 3000 dataset items under `data/` folder. They will be marked with the date of the dataset version.

## Citations

Please cite this repository if you the code.

```
@misc{tree-of-knowledge,
  author = {Xavier M},
  title = {Tree of Knowledge: ToK aka Tree of Knowledge dataset for Large Language Models LLM,
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/fblgit/tree-of-knowledge}},
}
```