Can We Train LLMs on Legal Docs So Developers Can Build an Enterprise-Grade Platform for Employees

November 14, 2023

In the ever-evolving world of technology, the quest for more efficient, cost-effective, and user-friendly solutions has led to the emergence of Legal Language Models (LLMs). These advanced AI models have the potential to revolutionize the way enterprises approach legal questions, providing a platform for employees to seek legal clarity effortlessly. But can we really train LLMs on legal documents to create such a platform? In this blog post, we'll explore the possibilities and challenges of harnessing the power of LLMs to make legal information more accessible within an enterprise setting.

The Rise of Legal Language Models

Legal Language Models, often based on the GPT (Generative Pre-trained Transformer) architecture, have gained significant attention in recent years. These models are designed to understand, generate, and interpret legal language, making them invaluable tools for legal professionals, organizations, and even the general public.

LLMs are trained on a vast corpora of legal documents, including contracts, statutes, case law, and regulations. As a result, they can comprehend and generate legal text with remarkable accuracy, demonstrating an understanding of nuanced legal concepts and the ability to offer meaningful insights into complex legal matters.

Applications of LLMs in Legal Tasks

LLMs, including models like GPT-3, have showcased their prowess in various legal tasks, making them a valuable resource in the legal domain. Here are some notable applications:

  1. Legal Judgment Prediction: LLMs can predict legal outcomes and assist in legal decision-making, enhancing the efficiency of legal professionals.
  2. Statutory Reasoning: Through advanced prompting techniques, LLMs can excel in statutory reasoning tasks, demonstrating their ability to handle complex legal language and reasoning.
  3. Legal Education: LLMs can aid in legal education by generating law school exams, providing ethical AI usage training, and assisting law professors with administrative tasks.
  4. Legal Advice: These models have the potential to provide affordable and prompt legal advice, making legal guidance more accessible to individuals.

The Enterprise Dilemma

Enterprises, regardless of their size or industry, constantly grapple with legal questions and concerns. Legal compliance, contract negotiations, intellectual property matters, and employment disputes are just a few examples of the issues that employees might encounter on a daily basis. Navigating the complexities of the legal system can be daunting, even for seasoned professionals, and accessing legal advice can be costly and time-consuming.

This is where LLMs have the potential to be game-changers. By training them on an enterprise's own legal documents and tailored to their specific needs, a custom LLM could provide a platform where employees can clarify legal questions without the need for direct consultation with legal experts. Let's delve into the potential benefits and challenges of this approach.

Benefits of Using LLMs for Legal Clarification within Enterprises

  1. Cost Reduction: Utilizing LLMs can significantly reduce the costs associated with legal consultations, as employees can get immediate answers to their queries without the need for expensive legal services.
  2. Time Efficiency: An LLM-powered platform could provide quick responses, saving valuable time for employees and helping them make more informed decisions promptly.
  3. Enhanced Legal Compliance: Enterprises can ensure better compliance with laws and regulations by offering employees a simple and efficient way to obtain legal guidance.
  4. Scalability: LLMs can handle a high volume of inquiries simultaneously, making them a scalable solution for large enterprises with diverse legal needs.

Legal Problems of Large Language Models

The integration of LLMs in the legal field has given rise to several legal challenges:

  1. Intellectual Property: LLMs can generate text that resembles copyrighted works, raising concerns about copyright ownership and the need for new legal frameworks.
  2. Data Privacy: LLMs are trained on extensive datasets, potentially containing personal or sensitive information, necessitating advanced data anonymization techniques to protect privacy.
  3. Bias and Discrimination: LLMs often inherit biases from their training data, which can lead to discriminatory outcomes. Addressing and mitigating these biases are essential for ensuring responsible deployment.

Challenges and Considerations

While the concept of using LLMs for legal clarification within enterprises is promising, it comes with its share of challenges and considerations:

  1. Privacy and Security: Handling sensitive legal documents within an LLM platform requires robust data security and privacy measures to protect confidential information.
  2. Customization: Training LLMs on an enterprise's specific legal documents is a complex task that may necessitate significant resources and expertise.
  3. Limitations: LLMs, while highly advanced, are not a substitute for legal expertise, especially in complex or highly specific cases. There will always be a need for human legal professionals.
  4. Ethical Concerns: Deploying LLMs in an enterprise raises questions about the ethical implications of relying on AI for legal advice, especially in situations where human judgment and empathy are crucial.

Data Resources for Large Language Models in Law

To effectively train and fine-tune LLMs for the legal domain, specialized data resources are essential. Some key datasets include:

  1. CAIL2018: This dataset, comprising millions of Chinese criminal cases, is a valuable resource for legal judgment prediction, multi-label classification, and explainable reasoning tasks.
  2. CaseHOLD: It offers multiple-choice questions covering various areas of law, complemented by domain pretrained models like BERT-Law and BERT-CaseLaw.
  3. LeCaRD: The Chinese Legal Case Retrieval Dataset specializes in Chinese legal terminology and provides relevance judgment criteria for case retrieval tasks.

Use Cases of LLMs in Enterprise

ChatLaw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases

The continuous development of artificial intelligence has paved the way for the proliferation of large-scale language models (LLMs). These models, such as ChatGPT, GPT4, LLaMA, and others, have demonstrated remarkable performance in various domains, offering immense potential for the field of law. However, the legal domain demands specialized LLMs that are effective, accurate, and up-to-date.

The legal field is a crucial part of society, governing human interactions, and upholding justice. Legal professionals rely on precise information to make informed decisions, interpret laws, and offer legal counsel. However, LLMs, even advanced ones like GPT4, often generate hallucinatory or nonsensical outputs when dealing with legal questions. Many believe that fine-tuning these models with specific legal knowledge will solve this problem, but in reality, it's not that straightforward.

Recognizing the need for a specialized legal LLM, particularly in Chinese, led to the development of models like ChatLaw. The key contributions of ChatLaw are:

  1. Effective Approach to Mitigate Hallucination: ChatLaw addresses hallucination by enhancing the training process and integrating four modules during inference: "consult," "reference," "self-suggestion," and "response." This integration of vertical models and knowledge bases through the reference module injects domain-specific knowledge, reducing hallucinations.
  2. Legal Feature Word Extraction Model: A model extracts legal feature words from everyday language, aiding in identifying legal contexts within user input.
  3. Legal Text Similarity Calculation Model: This model measures the similarity between user input and a dataset of 930,000 relevant legal case texts, facilitating the retrieval of similar legal texts for further analysis.
  4. Construction of a Chinese Legal Exam Testing Dataset: A dataset is curated for testing legal domain knowledge in Chinese, along with an ELO arena scoring mechanism to compare model performance in legal multiple-choice questions.

Furthermore, it's important to note that a single general-purpose legal LLM may not perform optimally across all tasks in this domain. Different models are trained for various scenarios, such as multiple-choice questions, keyword extraction, and question-answering. A big LLM controller dynamically selects the most suitable model for each user's request.


Constructing a comprehensive dataset is crucial. The dataset for ChatLaw is created using several methods, ensuring diversity and relevance:

  1. Collection of Original Legal Data: This includes legal news, social media content, and discussions from legal industry forums, offering insights into various legal topics and discussions.
  2. Construction Based on Legal Regulations and Judicial Interpretations: Legal regulations and judicial interpretations are incorporated into the dataset to reflect the legal framework accurately.
  3. Crawling Real Legal Consultation Data: Authentic legal consultation data, including real-world scenarios and questions, enrich the dataset.
  4. Construction of Multiple-Choice Questions for the Bar Exam: A set of multiple-choice questions designed specifically for the bar exam is included, covering various legal topics to test users' understanding and application of legal principles.

Once the dataset is collected, it undergoes rigorous cleaning to ensure high-quality and meaningful content.

Training Process

ChatLaw comprises three LLMs: ChatLaw, Keyword LLM, and Law LLM.

  • ChatLaw LLM: ChatLaw is fine-tuned based on Ziya-LLaMA-13B using Low-Rank Adaptation (LoRA) and a self-suggestion feature to alleviate hallucination issues. DeepSpeed reduces training costs.
  • Keyword LLM: This model extracts keywords from user queries, enhancing matching accuracy.
  • Law LLM: A BERT model is trained to extract legal provisions and judicial interpretations from user queries.

Experiment and Analysis

Evaluating an LLMs' performance is challenging, and a unique Elo ranking mechanism inspired by e-sports and Chatbot Arena was developed. The analysis reveals several insights:

  1. The inclusion of legal-related Q&A and statute data improves model performance on multiple-choice questions.
  2. Training models on specific task types significantly enhances their performance on those tasks.
  3. Models with more parameters tend to perform better on complex tasks requiring logical reasoning.


ChatLaw, a legal LLM, addresses the unique challenges of the legal domain by combining domain-specific knowledge with large language models. However, improvements in logical reasoning and generalization are needed. ChatLaw can be a valuable tool for legal professionals, but users should use it responsibly and ethically. Legal LLMs have the potential to revolutionize the field of law and make legal information more accessible to all.

Future Direction

The integration of LLMs into the legal domain has the potential to enhance legal processes, making them more efficient and accessible. However, it also brings legal challenges that must be addressed responsibly. Future research should focus on mitigating biases, ensuring transparency, developing specialized data resources, and establishing guidelines for the ethical use of LLMs in the legal domain.

The concept of using LLMs to build a platform where employees of an enterprise can clarify legal questions holds promise. By utilizing the capabilities of LLMs and addressing the legal challenges they present, such a platform can empower organizations to navigate legal complexities more efficiently and ethically. It represents an exciting frontier in the intersection of AI and law, promising to make legal knowledge more accessible to everyone.


Legal Language Models have the potential to transform how enterprises handle legal queries and concerns. By training these AI models on an enterprise's own legal documents, businesses can create a platform where employees can access legal clarity quickly and efficiently, reducing costs and saving time.

However, it's important to approach this potential transformation with careful consideration of the associated challenges, particularly regarding privacy, customization, and the ethical implications of relying on AI for legal guidance. While LLMs can be invaluable tools, they should complement, rather than replace, the expertise of human legal professionals. The fusion of AI and human knowledge has the potential to bring about a new era of legal efficiency within enterprises, fostering better compliance and informed decision-making.


  1. A Short Survey of Viewing Large Language Models in Legal Aspect
  2. ChatLaw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases

Latest Blogs
This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

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.

Reference Links

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

Reference Links

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:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. 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.
  3. 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.
  4. 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

This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
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.

Reference Links

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure