diff --git a/readme.md b/readme.md index 2228c7e..fcd4ed2 100644 --- a/readme.md +++ b/readme.md @@ -17,13 +17,13 @@ | Model | Reference | Output | Description | | :-------------------------- | ------------------------------------------------------------ | ------- | ------------------------------------------------------------ | -| Tree-Prompt | [📖](https://github.com/csinva/imodelsX/blob/master/demo_notebooks/tree_prompt.ipynb), [🗂️](http://csinva.io/imodelsX/treeprompt/treeprompt.html), [🔗](https://github.com/csinva/tree-prompt/tree/main), [📄]() | Explanation
+ Steering | Generates a tree of prompts to
steer an LLM (*Official*) | -| iPrompt | [📖](https://github.com/csinva/imodelsX/blob/master/demo_notebooks/iprompt.ipynb), [🗂️](http://csinva.io/imodelsX/iprompt/api.html#imodelsx.iprompt.api.explain_dataset_iprompt), [🔗](https://github.com/csinva/interpretable-autoprompting), [📄](https://arxiv.org/abs/2210.01848) | Explanation
+ Steering | Generates a prompt that
explains patterns in data (*Official*) | +| Tree-Prompt | [🗂️](http://csinva.io/imodelsX/treeprompt/treeprompt.html), [🔗](https://github.com/csinva/tree-prompt/tree/main), [📄](https://arxiv.org/abs/2310.14034), [📖](https://github.com/csinva/imodelsX/blob/master/demo_notebooks/tree_prompt.ipynb), | Explanation
+ Steering | Generates a tree of prompts to
steer an LLM (*Official*) | +| iPrompt | [🗂️](http://csinva.io/imodelsX/iprompt/api.html#imodelsx.iprompt.api.explain_dataset_iprompt), [🔗](https://github.com/csinva/interpretable-autoprompting), [📄](https://arxiv.org/abs/2210.01848), [📖](https://github.com/csinva/imodelsX/blob/master/demo_notebooks/iprompt.ipynb) | Explanation
+ Steering | Generates a prompt that
explains patterns in data (*Official*) | | AutoPrompt | ㅤㅤ[🗂️](), [🔗](https://github.com/ucinlp/autoprompt), [📄](https://arxiv.org/abs/2010.15980) | Explanation
+ Steering | Find a natural-language prompt
using input-gradients (⌛ In progress)| -| D3 | [📖](https://github.com/csinva/imodelsX/blob/master/demo_notebooks/d3.ipynb), [🗂️](http://csinva.io/imodelsX/d3/d3.html#imodelsx.d3.d3.explain_dataset_d3), [🔗](https://github.com/ruiqi-zhong/DescribeDistributionalDifferences), [📄](https://arxiv.org/abs/2201.12323) | Explanation | Explain the difference between two distributions | +| D3 | [🗂️](http://csinva.io/imodelsX/d3/d3.html#imodelsx.d3.d3.explain_dataset_d3), [🔗](https://github.com/ruiqi-zhong/DescribeDistributionalDifferences), [📄](https://arxiv.org/abs/2201.12323), [📖](https://github.com/csinva/imodelsX/blob/master/demo_notebooks/d3.ipynb) | Explanation | Explain the difference between two distributions | | SASC | ㅤㅤ[🗂️](https://csinva.io/imodelsX/sasc/api.html), [🔗](https://github.com/microsoft/automated-explanations), [📄](https://arxiv.org/abs/2305.09863) | Explanation | Explain a black-box text module
using an LLM (*Official*) | -| Aug-GAM | [📖](https://github.com/csinva/imodelsX/blob/master/demo_notebooks/aug_imodels.ipynb), [🗂️](https://csinva.io/imodelsX/auggam/auggam.html), [🔗](https://github.com/microsoft/aug-models), [📄](https://arxiv.org/abs/2209.11799) | Linear model | Fit better linear model using an LLM
to extract embeddings (*Official*) | -| Aug-Tree | [📖](https://github.com/csinva/imodelsX/blob/master/demo_notebooks/aug_imodels.ipynb), [🗂️](https://csinva.io/imodelsX/augtree/augtree.html), [🔗](https://github.com/microsoft/aug-models), [📄](https://arxiv.org/abs/2209.11799) | Decision tree | Fit better decision tree using an LLM
to expand features (*Official*) | +| Aug-GAM | [🗂️](https://csinva.io/imodelsX/auggam/auggam.html), [🔗](https://github.com/microsoft/aug-models), [📄](https://www.nature.com/articles/s41467-023-43713-1), [📖](https://github.com/csinva/imodelsX/blob/master/demo_notebooks/aug_imodels.ipynb) | Linear model | Fit better linear model using an LLM
to extract embeddings (*Official*) | +| Aug-Tree | [🗂️](https://csinva.io/imodelsX/augtree/augtree.html), [🔗](https://github.com/microsoft/aug-models), [📄](https://www.nature.com/articles/s41467-023-43713-1), [📖](https://github.com/csinva/imodelsX/blob/master/demo_notebooks/aug_imodels.ipynb) | Decision tree | Fit better decision tree using an LLM
to expand features (*Official*) |

📖Demo notebooks   🗂️ Doc   🔗 Reference code   📄 Research paper