Guide to Build an LLM Application for Law - Using SaulLM-7B and RAG Pipeline

May 6, 2024

What Is SaulLM-7B?

SaulLM is a first-of-its-kind large language model designed and trained specifically for the legal domain. It is built upon the architecture of the Mistral 7B model.

The model understands the intricacies of legal language better than general purpose LLMs, making it adept at responding to user queries about various legal tasks.

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Comparison of SaulLM with other models on a legal benchmark

The development of SaulLM involved a two-step process: continued pre-training on a legal corpus and instruction fine-tuning with a blend of general and legal instructions.

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Dataset and Pre-training:

To capture the nuances of legal language across diverse jurisdictions, a 30-billion token dataset was compiled. This included:

Legal Corpus: Publicly available legal datasets: FreeLaw, MultiLegal Pile, English EuroParl, GovInfo, Law Stack Exchange, Open Australian Legal Corpus, EU Legislation, UK Legislation, Court Transcripts, and UPSTO.
Replay Sources: Wikipedia, StackExchange, and GitHub to mitigate catastrophic forgetting.
Instruction Sources: Super natural Instructions and FLAN collection to enhance instruction following capabilities.

Instruction Fine-tuning: The Mistral 7B model was further trained on the cleaned legal corpus, enhancing its understanding of legal language and concepts. Instruction Fine-tuning included:

Instruction Mix: A combination of general and legal instructions was used to fine-tune the model.
General Instructions: SlimOrca, Meta Math Question Answering Instructions, General Conversations from UltraChat, and Code Instructions from Glaive Code Assistant v2.
Legal Instructions: Synthetically generated conversations addressing fundamental legal competencies across various legal document types. These were created using a Mistral-7B-Instruct model to transform legal texts and metadata into coherent dialogues.

Making a Streaming Law Chatbot with SaulLM-7B, Ollama, and Gradio

The SaulLM 7B requires around 7.7GB of GPU memory to operate. We can launch a T4 GPU node on https://myaccount.e2enetworks.com. That would suffice for our Chatbot application.

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We’ll use Ollama to run our LLM. Install it with the following command.


curl -fsSL https://ollama.com/install.sh | sh

This will launch a server on localhost:11434. Then pull the SaulLM model.


ollama pull adrienbrault/saul-instruct-v1:Q8_0

Install the following dependencies from your Juptyer Notebook.


pip install ollama gradio

Then we’ll write functions to be used in the Gradio UI. The first function takes in a user message and the chatbot’s history as input, and returns an empty string and chatbot history appended with the user message.

The second function takes in the chatbot’s history as input and modifies it by adding the LLM’s response. Instead of returning the response as a single text string, it streams it token by token. This makes the response more human friendly since users don’t have to wait for the results, and can start reading it as soon as the LLM starts producing the text.


from ollama import Client
client = Client(host='http://localhost:11434')

def user(user_message, history):
    return "", history + [[user_message, None]]

def bot(history):
    query = history[-1][0]

    history[-1][1] = ""

    try:
        for token in client.chat(model='adrienbrault/saul-instruct-v1:Q8_0', messages=[{'role': 'user', 'content': query}], stream=True):
            # Use yield to stream the response
            history[-1][1] += token['message']['content']
            yield history
    except Exception as e:
        # Stop the stream and handle the exception if necessary
        yield str(e)

Below is the code for the Gradio UI. Once the user submits the message, the user function is called, and it returns an empty string clearing the user message. It also prints the chatbot history on the screen which is currently only the user message.

Next, it calls the bot function which adds the response from the LLM to the chatbot history and prints it on the screen token by token.


import gradio as gr

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.Button("Clear")
    
    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot, chatbot, chatbot
    )
    clear.click(lambda: None, None, chatbot, queue=False)
    
demo.queue()
demo.launch(share=True)

Results

Here’s a screenshot of our Chatbot in action:

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We asked it a legal question to test it out.

Question: How does the 'doctrine of equivalents' apply in patent infringement cases across different jurisdictions, and what are the implications of this doctrine for international patent enforcement?

Response: The doctrine of equivalents is a legal principle used to determine patent infringement when a product or process is not an exact copy of the patented invention. This doctrine allows courts to find that two products or processes are equivalent if they perform substantially the same function in substantially the same way, even if there are minor differences in their design or structure.

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The application of the doctrine of equivalents varies across different jurisdictions. In some countries, like the United States and Canada, this doctrine is interpreted broadly, allowing for a more flexible approach to patent infringement cases. In contrast, other countries, such as the European Union, have a stricter interpretation of the doctrine, requiring a closer resemblance between the accused product or process and the patented invention.

The implications of this doctrine for international patent enforcement are significant. When a patent holder files an infringement case in a jurisdiction with a broad interpretation of the doctrine of equivalents, it may be easier for them to win the case, even if there are minor differences between the accused product or process and the patented invention. This can lead to more expensive and complex litigation processes across different countries, as patent holders may need to file cases in multiple jurisdictions with varying interpretations of this doctrine.

In addition, the doctrine of equivalents can have an impact on trade and international business relations. When a company is accused of infringing a patent in one jurisdiction but not another due to differences in the interpretation of this doctrine, it may face inconsistent legal outcomes in different countries. This can lead to confusion and uncertainty for businesses operating across borders, as they must navigate complex patent laws and interpretations in various jurisdictions.

To address these issues, there have been efforts to harmonize patent laws and the application of the doctrine of equivalents across different jurisdictions. For example, the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) established by the World Trade Organization (WTO) provides a minimum standard for the protection of intellectual property rights, including patents. However, countries retain the right to interpret and apply the doctrine of equivalents in their own legal systems, leading to variations in practice across different jurisdictions.

In conclusion, the doctrine of equivalents plays an important role in patent infringement cases across different jurisdictions, with varying interpretations that can impact international patent enforcement and trade relations. The harmonization of patent laws and the interpretation of this doctrine remains a challenge for businesses operating across borders, as they must navigate complex legal systems to protect their intellectual property rights.

Question2: In light of the California Consumer Privacy Act (CCPA), how do the obligations for businesses regarding consumer data privacy compare between California and the standards set by the General Data Protection Regulation (GDPR) in the European Union, and what are the specific challenges that businesses might face when trying to comply with both regulatory frameworks?

Response: The obligations for businesses regarding consumer data privacy under the CCPA and GDPR differ in several ways. Under the CCPA, consumers have the right to know what personal information is collected about them, the right to request that their personal information be deleted, the right to opt-out of the sale of their personal information, and the right to equal service and price, even if they exercise their privacy rights. The GDPR, on the other hand, grants individuals more extensive rights over their data, including the right to access, rectify, erasure, restrict processing, object to processing, data portability, and the right not to be subject to automated decision-making or profiling.

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One of the specific challenges businesses face when trying to comply with both regulatory frameworks is the differences in definitions and scopes. For example, the CCPA applies to for-profit entities that collect personal information from California residents, while the GDPR applies to controllers and processors established in the EU or targeting data subjects within the EU. Additionally, the scope of the GDPR is broader than the CCPA, as it covers all personal data, while the CCPA focuses on personal information.

Another challenge businesses face is the potential overlap in some obligations, such as the right to access and rectify personal data. While both regulations require businesses to provide consumers with information about their personal data processing practices, there are differences in how these rights are exercised, which could lead to confusion for businesses trying to comply with both frameworks.

Furthermore, the CCPA requires that businesses provide a clear and conspicuous link on their homepage titled "Do Not Sell My Personal Information," while the GDPR requires that businesses provide a link entitled "Privacy Policy" in the footer of their website. This difference in terminology could also lead to confusion for businesses trying to comply with both frameworks.

In summary, the obligations for businesses regarding consumer data privacy under the CCPA and GDPR differ in several ways, and businesses face challenges when trying to comply with both regulatory frameworks due to differences in definitions and scopes, overlapping obligations, and terminology.

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November 4, 2024

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.
CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels.

Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

This is a decorative image for: Constructing 3D objects through Deep Learning

October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

Highly calibrated cameras that take a photograph of the image from various angles.
Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website.

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Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts

October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
GAUDI also removes GANs pathologies like mode collapse and improved GAN.
GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
The scenes are thus synthesized by interpolation within the hidden space.
The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html