GenAI Interpretability is a powerful concept in the field of artificial intelligence that aims to provide insights and understanding into the decision-making process of the models. It focuses on explaining how and why a model arrives at a particular prediction or decision, making it easier for humans to interpret and trust the model's outputs.
I surveyed the list of known techniques available so far and captured them in the following mindmap.
Refer to the genai_interp.ipynb for a demo of selected techniques as below:
- Mehcanistic (Head, Model Visualisation)
- Feature Attribution (Integrated Gradients-based)
- Sample-based (Counterfactuals-based)
To install ollama locally (docker based): docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama --restart always ollama/ollama docker exec -it ollama ollama pull llama3 docker exec -it ollama ollama pull mxbai-embed-large