LawLLM: Law Large Language Model for the US Legal System

About

In the rapidly evolving field of legal analytics, finding relevant cases and accurately predicting judicial outcomes are challenging because of the complexity of legal language, which often includes specialized terminology, complex syntax, and historical context. Moreover, the subtle distinctions between similar and precedent cases require a deep understanding of legal knowledge. Researchers often conflate these concepts, making it difficult to develop specialized techniques to effectively address these nuanced tasks. In this paper, we introduce the Law Large Language Model (LawLLM), a multi-task model specifically designed for the US legal domain to address these challenges. LawLLM excels at Similar Case Retrieval (SCR), Precedent Case Recommendation (PCR), and Legal Judgment Prediction (LJP). By clearly distinguishing between precedent and similar cases, we provide essential clarity, guiding future research in developing specialized strategies for these tasks. We propose customized data preprocessing techniques for each task that transform raw legal data into a trainable format. Furthermore, we also use techniques such as in-context learning (ICL) and advanced information retrieval methods in LawLLM. The evaluation results demonstrate that LawLLM consistently outperforms existing baselines in both zero-shot and few-shot scenarios, offering unparalleled multi-task capabilities and filling critical gaps in the legal domain.

Requesting model access from META

1. Requesting model access from Google

visit this link and request the access to the Gemma-7B model.

2. Requesting model access from Hugging Face

Once request is approved, use the same email adrress to get the access of the model from HF here.

Once both requests are approved, follow the below directions.

Setup

1. Environment preparation

git clone https://github.com/Tizzzzy/Law_LLM.git

cd Law_LLM

pip install git+https://github.com/huggingface/transformers

# python 3.10 or higher recommended
pip install -r requirements.txt
pip install pyarrow~=12.0

huggingface-cli login

2. Authorising HF token

Once HF request to access the model has been approved, create hugging face token here

Run below code and enter your token. It will authenticate your HF account

>>> huggingface-cli login

or

>>> from huggingface_hub import login
>>> login(YOUR_HF_TOKEN)

Dataset

1. Preparing data for the model

2. Legal Case Vector Storage with ChromaDB

This project demonstrates how to store and retrieve legal case documents in a vectorized format using ChromaDB and OpenAI's embedding function. This allows for efficient similarity searches among legal case documents.

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have installed Python 3.8 or higher.
• You have a running instance of ChromaDB.
• You have an OpenAI API key to use the embedding function.

Embedding Legal Case Documents

First, you need to convert your legal case documents into vector embeddings using OpenAI's embedding function. Here is an example of how to do this in Python:

import openai

# Load your OpenAI API key
openai.api_key = 'your-api-key'

# Function to embed a document
def embed_document(document_text):
    response = openai.Embedding.create(
        input=document_text,
        model="text-similarity-babbage-001"  # Choose the appropriate model for your use case
    )
    embedding = response['data'][0]['embedding']
    return embedding

# Example usage: Embedding a legal case document
legal_case_text = "The legal case document text goes here."
legal_case_embedding = embed_document(legal_case_text)

Storing Embeddings in ChromaDB

After you have your document embeddings, you can store them in ChromaDB. Here's a sample code snippet for storing an embedding:

import chromadb

# Initialize ChromaDB client
client = chromadb.Client("your-chromadb-instance-url")

# Function to store an embedding into ChromaDB
def store_embedding(case_id, embedding):
    # Assuming 'cases' is the collection where you want to store your embeddings
    collection = client.collection('cases')
    document = {
        'id': case_id,
        'embedding': embedding
    }
    collection.insert(document)

# Example usage: Storing an embedding
case_id = "123456789"  # Unique identifier for the legal case document
store_embedding(case_id, legal_case_embedding)

Querying

To query the legal case documents by similarity, you can use the following function:

# Function to search for similar cases
def find_similar_cases(embedding, top_k=5):
    search_results = client.collection('cases').find_similar(
        embedding=embedding,
        k=top_k
    )
    return search_results

# Example usage: Finding similar cases
similar_cases = find_similar_cases(legal_case_embedding)
for case in similar_cases:
    print(case)  # Process the search results as needed

How to Run

After get all of the legal data from case.law into a folder. In preprocess folder, you should run train_test_split.py. This will seperate your data folder into a train folder and a text folder

For Legal Judgment Prediction

1. In LJP folder, run gpt_preprocess.py. This will preprocess all of the legal document in the folder and write it in a csv file. The csv file will have the format of:

File Name	Train Data

2. Notice that the code takes two file paths:
   • folder_path: your train folder or test folder from the Preprocess step.
   • csv_file_path: where do you wish to store the processed data.

For Precedent Case Recommondation

1. In PCR folder, run precedent_get.py. This will return a json file that contains all the processed data for PCR task.

2. Notice that the code takes three file paths:

   • folder_path: your train folder or test folder from the Preprocess step.
   • json_path: where do you wish to store the processed data.
   • cite_path: where do you wish to store the precedent data (Optional).

For Similar Case Retrival

1. In SCR folder, run SCR.py. This will return a json file that contains all the processed data for SCR task.

2. Notice that the code takes two file paths:

   • csv_file_path: your processed data path from the LJP task.
   • json_train_path: where do you wish to store the processed data.

Convert Three Tasks

1. In preprocess folder, run merge_data.py. This will return a json file that conatins all the processed data from all tasks.

2. Notice that the code takes four file paths:

   • LJP_file: your data path for LJP task.
   • SCR_file: your data path for SCR task.
   • PCR_file: your data path for PCR task.
   • train_file: where do you wish to store the final training data.

Finetune the Model

1. In train folder, run train.py.

   python train.py --file_path train_file.json --output_dir final_checkpoint

   • file_path is your combined training file.
   • output_dir is where you wish to store your model checkpoint.

2. If you want to modify any hyperparameters, feel free to do so.

Merge and Test the Model

1. In train folder, run LawLLM_merge_4bit.ipynb. This will merge your previously trained checkpoint with Gemma model.

2. Notice that the code takes two file paths:

   • checkpoint folder: your trained checkpoint folder.
   • output_merged_dir: where do you wish to store your final merged model.

3. After you model is ready, simply change the text to test the model.

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