JICL publication: A generative AI-based legal advice tool for small businesses in distress

· Thomas Wood
JICL publication: A generative AI-based legal advice tool for small businesses in distress

We are pleased to announce the publication of our paper A generative AI-based legal advice tool for small businesses in distress, in collaboration with an interdisciplinary team based in the UK and Hungary.

This paper describes the development and evaluation of the Insolvency Bot, a legal chatbot designed to provide reliable advice on corporate insolvency in England and Wales for small business owners.

We used Retrieval Augmented Generation (RAG) to enhance large language models with a curated knowledge base of 6,000 legal texts, including statutes, HMRC forms, and case law.

Thomas Wood, the director of Fast Data Science, was responsible for building the system in Python and implementing the machine learning models and vector embeddings that allow the bot to retrieve relevant legal information. Fast Data Science hosts the live version of the tool on our website at https://fastdatascience.com/insolvency

Abstract of the paper

We developed and tested the performance of a retrieval augmented generation (RAG) system for answering legal queries related to corporate insolvency in England and Wales. The Insolvency Bot relies on open-source legal information and HMRC forms to provide sound responses to a user’s query focusing on insolvency matters regulated by English law. We evaluated our bot head-to-head on an unseen test set against the unmodified versions of large language models (LLMs) gpt-3.5-turbo, gpt-4, or gpt-4o with a mark scheme similar to those used in examinations in law schools. The Insolvency Bot outperformed each unmodified LLM (p = 0.05%). An additional user experience survey suggested the need for creating two versions of the bot, one for lay people who expect practical and actionable advice and another for professionals with the relevant legal authorities. Our legal chatbot demonstrates the benefits of combining a generative AI system with a trusted knowledge base and shows future promise to cover cross-jurisdictional and insolvency-related queries and could be further improved in its technical architecture.

Citation

More information: https://pure.royalholloway.ac.uk/en/publications/a-generative-ai-based-legal-advice-tool-for-small-businesses-in-d/

Elevate Your Team with NLP Specialists

Unleash the potential of your NLP projects with the right talent. Post your job with us and attract candidates who are as passionate about natural language processing.

Hire NLP Experts

Can I use AI in court?
Generative ai

Can I use AI in court?

When can lawyers, litigants in person, and expert witnesses use AI in court documents? In the last few years in the UK, the USA, Canada, Ireland and other jurisdictions, cases have been reported where submissions were made to a court where the author of a document used generative AI tools such as ChatGPT to create those documents. This has wasted court time, resulted in submissions being rejected or even resulted in changes to cost awards.

Semantic leakage
Generative ai

Semantic leakage

A person has recently returned from a camping trip and has a fever. Should a doctor diagnose flu or Lyme disease? Would this be any different if they had not mentioned their camping trip? Here’s how LLMs differ from human experts.

Predicting Customer Churn using Machine Learning and AI
Data science consultingAi for business

Predicting Customer Churn using Machine Learning and AI

How can you predict customer churn using machine learning and AI? In an earlier blog post, I introduced the concept of customer churn. Here, I’d like to dive into customer churn prediction in more detail and show how we can easily and simply use AI to predict customer churn.

What we can do for you

Transform Unstructured Data into Actionable Insights

Contact us