TalkToModel

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TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!

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# TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations

[![Python application](https://github.com/dylan-slack/TalkToModel/actions/workflows/python-app.yml/badge.svg)](https://github.com/dylan-slack/TalkToModel/actions/workflows/python-app.yml) [![arXiv](https://img.shields.io/badge/arXiv-2207.04154-b31b1b.svg)](https://arxiv.org/abs/2207.04154)

Welcome to the [TalkToModel paper](https://arxiv.org/abs/2207.04154) page! The goal of this project is to enable anyone to understand the predictions of a trained machine learning model through a natural language conversation. Ultimately, it is a platform for **conversational XAI**!

## Links

- Check out the [arXiv TalkToModel paper](https://arxiv.org/abs/2207.04154) ✨
- Check out the [github code](https://github.com/dylan-slack/TalkToModel) 🖥️
- We are hosting a [demo of TalkToModel](https://nlp.ics.uci.edu/talk-to-healthcare-model/) on the diabetes prediction task 🚀

If you found this work useful, please cite us!

```bibtex
@Article{Slack2023,
author={Slack, Dylan
and Krishna, Satyapriya
and Lakkaraju, Himabindu
and Singh, Sameer},
title={Explaining machine learning models with interactive natural language conversations using TalkToModel},
journal={Nature Machine Intelligence},
year={2023},
month={Jul},
day={27},
abstract={Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability methods because they do not know which explanation to choose and how to interpret the explanation. Here we address the challenge of using explainability methods by proposing TalkToModel: an interactive dialogue system that explains ML models through natural language conversations. TalkToModel consists of three components: an adaptive dialogue engine that interprets natural language and generates meaningful responses; an execution component that constructs the explanations used in the conversation; and a conversational interface. In real-world evaluations, 73{\%} of healthcare workers agreed they would use TalkToModel over existing systems for understanding a disease prediction model, and 85{\%} of ML professionals agreed TalkToModel was easier to use, demonstrating that TalkToModel is highly effective for model explainability.},
issn={2522-5839},
doi={10.1038/s42256-023-00692-8},
url={https://doi.org/10.1038/s42256-023-00692-8}
}
```

[UPDATE] This work won an honorable mention outstanding paper at the TSRML Workshop at NeurIPS 🎉

We additionally wrote a [precursor paper](https://arxiv.org/abs/2202.01875) about domain experts needs for understanding models, that helped inspire this work. It's called *Rethinking Explainability as a Dialogue: A Practitioner's Perspective*. Check that out as well!

## Table of Contents

- [Overview](#Overview)
- [Installation](#Installation)
- [Running the TalkToModel Application](#running-the-talktomodel-application)
- [Running On Your Own Model & Dataset](#running-on-your-own-model--dataset)
- [Experiments](#experiments)
- [Development](#development)

## Overview

Here follows a brief overview of the purpose and scope of the system.

### Purpose

As machine learning models are being increasingly integrated in our day-to-day lives, it becomes important that anyone can interact with and understand them. TalkToModel helps realize this goal and enables *anyone* to chat with a machine learning model to understand the model's predictions.

Please [read our paper](https://arxiv.org/abs/2207.04154) for more motivation and details about how the system works.

### Scope

TalkToModel supports *tabular* models and datasets. For example, you could use the system to chat with a random forest trained on a loan prediction task but not BERT trained on a sentiment analysis task, in the system's current form.

## Installation

To run TalkToModel, you can either setup a conda environment or use Docker to directly run the Flask App.

Note, for GPU inference, the environment requires **CUDA 11.3**. If you do not have cuda 11.3 and try and run the docker application, it will crash! One way around this is to switch to cpu inference. It will be a bit slower ([instructions](#gpu-availability)), but should work.

### Docker

If you want to use Docker, you can skip this setup step ⏭️

### Conda

Create the environment and install dependencies.

```shell
conda create -n ttm python=3.9
conda activate ttm
```

Install the requirements

```shell
pip install -r requirements.txt
```

Nice work 👍

## Running A Pre-Configured TalkToModel Demo

Next, we discuss how to run the Flask Application on one of the datasets and models from [the paper](https://arxiv.org/abs/2207.04154). These include a diabetes, crime, and credit prediction task.

### Configuration

Here, we talk about choosing a demo + parsing model. If you just want to run the demo on the diabetes dataset, you can skip to [Running With Conda](#running-with-conda) or [Running With Docker](#running-with-docker).

#### Choosing a Demo & Parsing Model
This software is configured using [gin-config](https://github.com/google/gin-config). Global parameters are stored in `./global_config.gin` and demo specific parameters are stored in the `./configs` directory, e.g., `./configs/diabetes-config.gin` for the diabetes prediction demo.

This repo comes configured using a fine-tuned `t5-small` model for the diabetes prediction task, but this can be changed by modifying `./global_config.gin`:

```shell
GlobalArgs.config = "./configs/{demo}-config.gin"
```

and changing `{demo}` to one of `diabetes`, `compas`, or `german` respectively. Further, we provide our best fine-tuned `t5-small` and `t5-large` parsing models on the huggingface hub. These can be selected by modifying `./configs/{demo}-config.gin`:

```shell
# For the t5 small model
ExplainBot.parsing_model_name = "ucinlp/{demo}-t5-small"
# For the t5 large model
ExplainBot.parsing_model_name = "ucinlp/{demo}-t5-large"
```

and the respective parsing model will be downloaded from the hub automatically.

#### GPU Availability

By default, the system will try to push the models to a cuda device and will crash if one isn't available. To switch to CPU inference or to use a different cuda device, modify `./parsing/t5/gin_configs/t5-large.gin`

```shell
load_t5_params.device = "{device}"
```

where `{device}` is the device you want (e.g., `cpu`).

### Running With Conda

If you installed the conda environment, to launch the [Flask](https://flask.palletsprojects.com/en/2.2.x/) web app you can run
```shell
python flask_app.py
```

### Running With Docker

If you want to run with [Docker](https://www.docker.com), you can build the docker app

```shell
sudo docker build -t ttm .
```

And then run the image

```shell
sudo docker run -d -p 4000:4000 ttm
```

### Use It!

It might take a minute or two to build the application. The reason is that we cache a fair number of computations (mostly explanations) beforehand to improve the realtime user experience. For reference, on my M1 macbook pro, building from scratch takes about 5 minutes. However, after your first time running a demo, these computations will be stored in the `./cache` folder, and it is not necessary to compute them again, so startup should be very quick.

That's it! The app should be running 💥

## Experiments

Here, we discuss running experiments from the paper. For these experiments, make sure to set

```
ExplainBot.skip_prompts = False
```

in each of the `diabetes`, `german`, and `compas` gin config files, so that the system generates the prompts for each dataset. We set this to `True` for the fine-tuned demo parsing models, because it is unnecessary in this case and speeds up the startup time significantly.

### Fine-tuning Models

To fine-tune a parsing model, run

```shell
python parsing/t5/start_fine_tuning.py --gin parsing/t5/gin_configs/t5-{small, base, large}.gin --dataset {diabetes, german, compas}
```

where `{small, base, large}` and `{diabetes, german, compas}` are one of the values in the set. Note, these experiments require use [Weights & Biases](https://wandb.ai/site) to track training and the best validation model.

For simplicity, we also provide all the best validation pre-trained models for download on huggingface: [https://huggingface.co/dslack/all-finetuned-ttm-models](https://huggingface.co/dslack/all-finetuned-ttm-models). You can download these models from the provided zip file, and unzip the models to `./parsing/t5/models`.

### Evaluating Gold Parsing Accuracy

With all the models downloaded, you can compute the parsing accuracies by running

```shell
python experiments/generate_parsing_results.py
```

The results will be deposited in the `./experiments/results_store` directory.

## Running on Your Own Model & Dataset

Please see the tutorial `./tutorials/running-on-your-own-model.ipynb` for a step-by-step walk-through about how to do this, and the different options you have for setting up the conversation.

## Development

TalkToModel can be extended to include new functionality pretty easily. Please see `./tutorials/extending-ttm.md` for a walk-through on how to extend the system.

## Testing

You can run the tests by running `pytest` from the base directory.

## Citation

Cite us 🫶

## Contact

You can reach out to [dslack@uci.edu](dslack@uci.edu) with any questions or issues you're running into.

Please do reach out or submit an issue, because I would love to help you get it running, especially on your own models and data ❤️!

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