Update documentation

PiperOrigin-RevId: 610789991

website/sphinx_src/components.md

@@ -433,7 +433,7 @@ You don't have to call the field "label", and it's okay if this field isn't
 present in the *dataset* - as long as it's something that the model will
 recognize and use as the target to derive gradients.
 
-## Sequence salience
+## Sequence Salience
 
 Sequence salience generalizes token-based salience to text-to-text models,
 allowing you to explain the impact of the prompt tokens on parts of the model
@@ -468,7 +468,8 @@ https://ai.google.dev/responsible/model_behavior.
 
 **Code:**
 
-*   Demo:
+*   LIT-for-Gemma Colab: [`lit_gemma.ipynb`](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb)
+*   Demo binary:
     [`lm_salience_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py)
 *   KerasNLP model wrappers:
     [`instrumented_keras_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py)

website/sphinx_src/demos.md

@@ -80,6 +80,20 @@ https://pair-code.github.io/lit/tutorials/sentiment
 
 ## Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} -->
 
+### Gemma <!-- DO NOT REMOVE {#gemma .demo-header} -->
+
+**Code:** [examples/lm_salience_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py)
+
+*   Supports Gemma 2B and 7B models using KerasNLP and TensorFlow.
+*   Interactively debug LLM prompts using
+    [sequence salience](./components.md#sequence-salience).
+*   Multiple salience methods (grad-l2 and grad-dot-input), at multiple
+    granularities: token-, word-, sentence-, and paragraph-level.
+
+Tip: check out the in-depth walkthrough at
+https://ai.google.dev/responsible/model_behavior, part of the Responsible
+Generative AI Toolkit.
+
 ### T5 <!-- DO NOT REMOVE {#t5 .demo-header} -->
 
 **Hosted instance:** https://pair-code.github.io/lit/demos/t5.html \

website/sphinx_src/index.md

@@ -8,6 +8,24 @@ Welcome to 🔥LIT, the Learning Interpretability Tool!
 
 If you want to jump in and start playing with the LIT UI, check out the hosted demos at https://pair-code.github.io/lit/demos/.
 
+## Research
+
+Found LIT useful in your research? Please cite our
+[system demonstration paper](https://aclanthology.org/2020.emnlp-demos.15/)!
+
+```
+@misc{tenney2020language,
+    title={The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for {NLP} Models},
+    author={Ian Tenney and James Wexler and Jasmijn Bastings and Tolga Bolukbasi and Andy Coenen and Sebastian Gehrmann and Ellen Jiang and Mahima Pushkarna and Carey Radebaugh and Emily Reif and Ann Yuan},
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+    year = "2020",
+    publisher = "Association for Computational Linguistics",
+    pages = "107--118",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.15",
+}
+```
+
+
 ```{toctree}
    :maxdepth: 2