The primary objective is to develop a Legal LLM, leveraging both proprietary contracts and public datasets like CUAD. This model will assist in drafting and reviewing contracts, with continuous learning capabilities from user feedback.
We are a boutique legal and commercial consulting firm with highly experienced partners. In order for us to stay competitive, we would like to limit redundant work by investing time and effort in training our own model based on the work we do in order for us to limit the time spent on new projects that includes work already provided previously to another client.
Accordingly, we are looking to train an existing LLM model (e.g. Llama2 or another open source LLM model) based on our repository of contracts as well as existing data sets like CUAD.
The goal is to create a LLM with a baseline contract understanding that we use in our daily work to draft and review contracts. Based on this baseline, we then want to be able to use and to continuously train the model by scoring its suggestions and/or by providing it with new or better solutions when drafting or reviewing specific clauses so that it keeps learning from our high expertise.
In addition, to the extent feasible, we would like to feed the model with laws, regulations and guidelines as well as specific legal books in order to advance its understanding.
It's key for us to understand and that we find the right balance between hardware required to run and train the model and the accuracy of the model. The reason for this is that the model won't be allowed to run in public cloud once we start using it with our clients.
We are open to and would appreciate suggestions on how to approach this project in a way where we start "small" and then keep building the model while be utilize the model in our daily work.
We expect that once the project is complete, we will be needing continues support and services in order to ensure the ongoing development of the model.
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