Add a tutorial for LLM Attribution (#1228)

Summary:
as title

The tutorial only demonstrate the perturbation-based algorithms. Will add the gradient-based demo later.

rendered notebook:
https://github.com/aobo-y/captum/blob/llm/tutorials/Llama2_LLM_Attribution.ipynb

Pull Request resolved: #1228

Reviewed By: vivekmig

Differential Revision: D52476602

Pulled By: aobo-y

fbshipit-source-id: 5565fc41c163cff1ddbf32ac8def52aba38b7d1e

Author: aobo-y

tutorials/Llama2_LLM_Attribution.ipynb

@@ -85,6 +85,11 @@ class TutorialHome extends React.Component {
                 Using Captum and Integrated Gradients we interpret the output of several test questions and analyze the attribution scores
                 of the text and visual parts of the model. Find the tutorial <a href="Multimodal_VQA_Interpret">here</a>.
 
+                <h4>Understanding Llama2 with Captum LLM Attribution:</h4>
+                This tutorial demonstrates how to easily use the LLM attribution functionality to interpret the large langague models (LLM) in text generation.
+                It takes Llama2 as the example and shows the step-by-step improvements from the basic attribution setting to more advanced techniques.
+                Find the tutorial <a href="Llama2_LLM_Attribution">here</a>.
+
                 <h4>Interpreting question answering with BERT Part 1:</h4>
                 This tutorial demonstrates how to use Captum to interpret a BERT model for question answering.
                 We use a pre-trained model from Hugging Face fine-tuned on the SQUAD dataset and show how to use hooks to

website/pages/tutorials/index.js

@@ -46,6 +46,10 @@
       "id": "Image_and_Text_Classification_LIME",
       "title": "Interpreting vision and text models with LIME"
     },
+    {
+      "id": "Llama2_LLM_Attribution",
+      "title": "Understanding Llama2 with Captum LLM Attribution"
+    },
     {
       "title": "Interpreting BERT",
       "children": [