When authorized analysis firm LexisNexis created its AI assistant Protégé, it needed to determine one of the simplest ways to leverage its experience with out deploying a big mannequin.
Protégé goals to assist legal professionals, associates and paralegals write and proof authorized paperwork and make sure that something they cite in complaints and briefs is correct. Nonetheless, LexisNexis didn’t need a common authorized AI assistant; they needed to construct one which learns a agency’s workflow and is extra customizable.
LexisNexis noticed the chance to convey the ability of enormous language fashions (LLMs) from Anthropic and Mistral and discover the perfect fashions that reply consumer questions the perfect, Jeff Riehl, CTO of LexisNexis Authorized and Skilled, informed VentureBeat.
“We use the best model for the specific use case as part of our multi-model approach. We use the model that provides the best result with the fastest response time,” Riehl mentioned. “For some use cases, that will be a small language model like Mistral or we perform distillation to improve performance and reduce cost.”
Whereas LLMs nonetheless present worth in constructing AI purposes, some organizations flip to utilizing small language fashions (SLMs) or distilling LLMs to turn into small variations of the identical mannequin.
Distillation, the place an LLM “teaches” a smaller mannequin, has turn into a preferred methodology for a lot of organizations.
Small fashions usually work greatest for apps like chatbots or easy code completion, which is what LexisNexis needed to make use of for Protégé.
This isn’t the primary time LexisNexis constructed AI purposes, even earlier than launching its authorized analysis hub LexisNexis + AI in July 2024.
“We have used a lot of AI in the past, which was more around natural language processing, some deep learning and machine learning,” Riehl mentioned. “That really changed in November 2022 when ChatGPT was launched, because prior to that, a lot of the AI capabilities were kind of behind the scenes. But once ChatGPT came out, the generative capabilities, the conversational capabilities of it was very, very intriguing to us.”
Small, fine-tuned fashions and mannequin routing
Riehl mentioned LexisNexis makes use of totally different fashions from a lot of the main mannequin suppliers when constructing its AI platforms. LexisNexis + AI used Claude fashions from Anthropic, OpenAI’s GPT fashions and a mannequin from Mistral.
This multimodal strategy helped break down every activity customers needed to carry out on the platform. To do that, LexisNexis needed to architect its platform to modify between fashions.
“We would break down whatever task was being performed into individual components, and then we would identify the best large language model to support that component. One example of that is we will use Mistral to assess the query that the user entered in,” Riehl mentioned.
For Protégé, the corporate needed quicker response instances and fashions extra fine-tuned for authorized use instances. So it turned to what Riehl calls “fine-tuned” variations of fashions, basically smaller weight variations of LLMs or distilled fashions.
“You don’t need GPT-4o to do the assessment of a query, so we use it for more sophisticated work, and we switch models out,” he mentioned.
When a consumer asks Protégé a query a few particular case, the primary mannequin it pings is a fine-tuned Mistral “for assessing the query, then determining what the purpose and intent of that query is” earlier than switching to the mannequin greatest suited to finish the duty. Riehl mentioned the subsequent mannequin might be an LLM that generates new queries for the search engine or one other mannequin that summarizes outcomes.
Proper now, LexisNexis largely depends on a fine-tuned Mistral mannequin although Riehl mentioned it used a fine-tuned model of Claude “when it first came out; we are not using it in the product today but in other ways.” LexisNexis can be desirous about utilizing different OpenAI fashions particularly because the firm got here out with new reinforcement fine-tuning capabilities final yr. LexisNexis is within the technique of evaluating OpenAI’s reasoning fashions together with o3 for its platforms.
Riehl added that it might additionally take a look at utilizing Gemini fashions from Google.
LexisNexis backs all of its AI platforms with its personal data graph to carry out retrieval augmented technology (RAG) capabilities, particularly as Protégé may assist launch agentic processes later.
The AI authorized suite
Even earlier than the arrival of generative AI, LexisNexis examined the opportunity of placing chatbots to work within the authorized trade. In 2017, the corporate examined an AI assistant that will compete with IBM’s Watson-powered Ross and Protégé sits within the firm’s LexisNexis + AI platform, which brings collectively the AI providers of LexisNexis.
Protégé helps regulation corporations with duties that paralegals or associates are likely to do. It helps write authorized briefs and complaints which can be grounded in corporations’ paperwork and information, recommend authorized workflow subsequent steps, recommend new prompts to refine searches, draft questions for depositions and discovery, hyperlink quotes in filings for accuracy, generate timelines and, in fact, summarize advanced authorized paperwork.
“We see Protégé as the initial step in personalization and agentic capabilities,” Riehl mentioned. “Think about the different types of lawyers: M&A, litigators, real estate. It’s going to continue to get more and more personalized based on the specific task you do. Our vision is that every legal professional will have a personal assistant to help them do their job based on what they do, not what other lawyers do.”
Protégé now competes in opposition to different authorized analysis and expertise platforms. Thomson Reuters personalized OpenAI’s o1-mini-model for its CoCounsel authorized assistant. Harvey, which raised $300 million from buyers together with LexisNexis, additionally has a authorized AI assistant.
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