Updated README

README.md

@@ -4,37 +4,27 @@
 [![CI](https://github.com/souradipp76/MM-PoE/actions/workflows/main.yml/badge.svg)](https://github.com/souradipp76/MM-PoE/actions/workflows/main.yml)
 
 
+**Multiple Choice Reasoning tool via. Process of Elimination using Multi-Modal models**
 
-## License
-
-Read the [LICENSE](LICENSE) file.
-
-**Visual analysis and diagnostic tools to facilitate machine learning model selection.**
 
-
-## What is MM-POE? Statement of Need
+## What is MM-PoE?
 
 **Statement of Need**
 
-Language models (LMs) excel at in-context learning for multiple choice reasoning tasks but often treat all options equally, unlike humans who typically eliminate incorrect choices before selecting the correct answer. This discrepancy can limit the effectiveness of LMs in accurately solving such tasks. To address this, we introduce the Process of Elimination (POE), a two-step scoring method designed to enhance LM performance by mimicking human reasoning strategies. 
+Language models (LMs) excel at in-context learning for multiple choice reasoning tasks but often treat all options equally, unlike humans who typically eliminate incorrect choices before selecting the correct answer. Same is true in case of visual question answering tasks with multiple choices. This discrepancy can limit the effectiveness of vision language models in accurately solving such tasks. To address this, we introduce Multi-Modal Process of Elimination (MM-PoE), a two-step scoring method designed to enhance VLM performance by mimicking human reasoning strategies in multi-modal settings. 
 
-In the first step, POE evaluates and scores each option, systematically eliminating those that appear incorrect. The second step involves masking these eliminated options, allowing the LM to focus solely on the remaining viable choices to make a final prediction. Our zero-shot experiments across eight reasoning tasks demonstrate POE's effectiveness, particularly excelling in logical reasoning scenarios. Additionally, POE proves adaptable to few-shot settings and is compatible with large language models (LLMs) like ChatGPT.
+In the first step, the method evaluates and scores each option, systematically eliminating those that appear incorrect. The second step involves masking these eliminated options, allowing the VLM to focus solely on the remaining viable choices to make a final prediction. Our zero-shot experiments across three datasets demonstrate MM-PoE's effectiveness, particularly excelling in logical reasoning scenarios . Additionally, MM-PoE proves adaptable to few-shot settings and is compatible with large language models (LLMs) like ChatGPT.
 
-By implementing POE, researchers and practitioners can significantly improve the accuracy and reliability of LMs in multiple choice reasoning tasks, making it a valuable tool for advancing machine learning model selection and evaluation.
+By implementing MM-PoE, researchers and practitioners can experiment and significantly improve the accuracy and reliability of VLMs in multiple choice reasoning tasks, making it a valuable tool for advancing machine learning models for visual reasoning.
 
 ## Installing MM-POE
 
 ### Install it from PyPI
 
-```bash
-pip install mm_poe
-```
-### Install it from source
+The simplest way to install MM-PoE and its dependencies is from PyPI with pip, Python's preferred package installer.
 
 ```bash
-$ git clone https://github.com/souradipp76/MM-PoE.git
-$ cd MM-PoE
-$ make install
+$ pip install mm_poe
 ```
 
 In order to upgrade MM-POE to the latest version, use pip as follows.
@@ -43,28 +33,52 @@ In order to upgrade MM-POE to the latest version, use pip as follows.
 $ pip install -U mm_poe
 ```
 
-## Using Yellowbrick
+### Install it from source
+
+You can also install MM-PoE from source as follows.
+
+```bash
+$ git clone https://github.com/souradipp76/MM-PoE.git
+$ cd MM-PoE
+$ make install
+```
+
+## Usage
 
-Here is a typical example usage of MM-POE:
+Here is a typical example usage of MM-PoE.
 
-### Running the CLI
+### Running CLI
 
 ```bash
 $ python -m mm_poe
 #or
 $ mm_poe
 ```
 
-## Contributing to MM-POE
+### Running Experiments
+
+```bash
+$ bash 7_main_exp.sh
+#or
+$ bash 9_mask_vqa.sh
+#or
+$ bash 11_few_shot_vqa.sh
+```
+
+## Contributing
 
 MM-POE is an open-source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project.
 
 If you are interested in contributing, read the [CONTRIBUTING.md](CONTRIBUTING.md) file.
 
 - Submit a bug report or feature request on [GitHub Issues](https://github.com/souradipp76/MM-PoE/issues).
 - Add to the documentation or help with our website.
-- Write [unit or integration tests]() for our project.
+- Write unit or integration tests for our project.
 - Answer questions on our issues, mailing list, Stack Overflow, and elsewhere.
 - Write a blog post, tweet, or share our project with others.
 
 As you can see, there are lots of ways to get involved, and we would be very happy for you to join us!
+
+## License
+
+Read the [LICENSE](LICENSE) file.