Thomson Reuters Develops Legal AI Model 'Thomson' with $40 Million Investment

Thomson Reuters has developed its own large language model (LLM) named 'Thomson' with an investment of $40 million. This model is a corporate model specialized in legal, tax, and regulatory data, developed independently without using a general-purpose AI model. Thomson Reuters stated that this model is the first proprietary LLM developed in-house. The model started from an open-source base and underwent intermediate and subsequent training with the company's specialized content. Joel Huron, the Chief Technology Officer, mentioned that Thomson provides a more efficient and controllable intelligence, deeply specialized for specific tasks. The first application of Thomson is in the legal AI service CoCounsel's document analysis feature, used in structured tasks. Thomson Reuters revealed that less than 10% of the training data for Thomson came from its own content. Large language models are AI models that learn from large-scale documents to perform tasks such as question answering, summarization, and document analysis. The company also released a small open-weight model on Hugging Face. Professor Jonathan Choi evaluated that Thomson is preferred over other models. This case suggests that the performance of enterprise AI must consider not only the capabilities but also data storage locations and security requirements.

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