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--- |
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license: gpl-3.0 |
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task_categories: |
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- summarization |
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- text-classification |
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language: |
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- en |
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tags: |
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- legal |
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pretty_name: Maple Bill Summarization and Tagging |
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size_categories: |
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- 100M<n<1B |
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configs: |
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- config_name: main_data |
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data_files: "demoapp/all_bills.csv" |
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--- |
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# MAPLE (Bill Summarization, Tagging, Explanation) |
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In this project, we generate summaries and category tags for of Massachusetts bills for [MAPLE Platform](https://www.mapletestimony.org/). The goal is to simplify the legal language and content to make it comprehensible for a broader audience (9th-grade comprehension level) by exploring different ML and LLM services. |
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This repository contains a pipeline from taking bills from Massachusetts legislature, generating summaries and category tags leveraging different the Massachusetts General Law sections, creating a dashboard to display and save the generated texts, to deploying and integrating into MAPLE platform. |
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## Roadmap of Repository Directories |
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* [Documentation](https://github.com/vynpt/ml-maple-bill-summarization/tree/dev/Documentation): |
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```Research.md```: our research on large language models and evaluation methods we planned to use for this project. |
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```Documentation MAPLE.pdf```: includes detail operation of our model for future use and improvement. |
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* [EDA](https://github.com/vynpt/ml-maple-bill-summarization/tree/dev/EDA): the notebook ```eda.ipynb``` includes our work from scraping data that takes bills from MAPLE Swagger API, creating a dataframe to clean and process data, making visualizations to analyze data and explore characteristics of the dataset. |
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* [demoapp](https://github.com/vynpt/ml-maple-bill-summarization/tree/dev/demoapp): |
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```app.py```: contains the codes of the LLM service we used and the wepapp we made using Streamlit. The webapp allows user to search for all bills. |
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```app2.py```: we test on top 12 bills from MAPLE website. We extract information from [Massachusetts General Law](https://malegislature.gov/Laws/GeneralLaws) to add context for the summaries of these bills. |
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Other files: helper files to be imported in the above two Python app files. |
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* [Prompts Engineering](https://github.com/vynpt/ml-maple-bill-summarization/tree/dev/Prompts%20Engineering): ```prompts.md``` stores all prompts that we tested. |
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* [Tagging](https://github.com/vynpt/ml-maple-bill-summarization/tree/dev/Tagging): contains the list of categories and tags. |
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* [Deployment](https://github.com/vynpt/ml-maple-bill-summarization/tree/main/Deployment): contains the link of our Streamlit deployed webapp. |
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## Ethical Implications |
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The dataset used for this project is fully open sourced and can be access through Mass General Laws API. |
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Our team and MAPLE agree about putting disclaimer that this text is AI-generated. |
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Although we make use of open source transformers to evaluate hallucination with Vectara, it is important to have experts and human evaluation to further maintain a trustworthy LLM system. |
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## Resources and Citation |
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* https://huggingface.co/docs/transformers/tasks/summarization |
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* https://huggingface.co/vectara/hallucination_evaluation_model |
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* https://github.com/vectara/hallucination-leaderboard |
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* https://www.nocode.ai/llms-undesirable-outputs/ |
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* https://learn.deeplearning.ai/ |
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* https://blog.langchain.dev/espilla-x-langchain-retrieval-augmented-generation-rag-in-llm-powered-question-answering-pipelines/ |
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## Team Members |
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Vy Nguyen - Email: nptv1207@bu.edu |
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Andy Yang - Email: ayang903@bu.edu |
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Gauri Bhandarwar - Email: gaurib3@bu.edu |
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Weining Mai - Email: weimai@bu.edu |
