DeepSeek's Impact on the Legal Sector
Since early 2025, Hangzhou-based AI startup DeepSeek has attracted global attention with its advanced AI solutions and broad application potential. Major tech companies such as Baidu, WeChat, and Huawei Cloud have integrated DeepSeek's technology into their products. This trend has also reached government services; notably, Beijing’s Fengtai District deployed DeepSeek's large language model (LLM) on its government cloud, launching the "Feng Xiao Zheng" digital assistant to enhance public service efficiency.
Within legal services, DeepSeek's influence is growing steadily. Law firms and legal tech companies recognize its potential and are exploring integrations that balance precision and efficiency. Courts and procuratorates across various regions have started training programs and deployments to boost judicial work. In March, China’s Judicial Convenience Platform integrated DeepSeek to offer online legal consultations.
Different Approaches in Legal Tech
Kevin Wang, COO of legal tech firm L-Expert, highlights that DeepSeek's open-source availability and commercial options offer performance comparable to top-tier LLMs at a fraction of the cost. This has led many Chinese law firms and tech providers to adopt DeepSeek for enhanced legal database searches, document drafting, and contract review.
"Law firms are eager to experiment and develop AI tools that truly improve productivity," Wang notes. Managing partners increasingly prioritize AI-enabled products, collaborating with L-Expert to build and test applications. L-Expert itself has locally deployed large language models powered by DeepSeek to improve AI assistants, cross-database searches with automatic organization, and automated document management.
Yingke Law Firm announced in March a full integration with DeepSeek, becoming one of the first firms in China to connect DeepSeek with legal services. Yingke uses the DeepSeek-R1 inference model to introduce intelligent solutions across multiple practice areas. They have enriched DeepSeek-R1 with proprietary legal data, including lawyer profiles, case libraries, regulatory databases, and contract templates, creating a comprehensive knowledge framework enhanced by specialized legal training.
Yingke’s Multi-Scenario Deployment
- Knowledge System Construction: Consolidating fragmented legal resources into an integrated database for more efficient knowledge management.
- Regulatory Research: Using real-time updates and precise maintenance of regulatory databases, ensuring accuracy and authority.
- Client Communication: Organizing inquiries, extracting keywords, and intelligently matching clients with lawyers based on case type and expertise.
- Case Retrieval: Categorizing and retrieving historical cases to provide data-driven support for litigation strategies.
- Contract Services: Improving contract generation and review with AI-powered risk identification, clause generation, and version comparison.
- Internal Management: Managing multidimensional lawyer information to optimize business allocation and foster collaboration.
The Challenge of Hallucinations
DeepSeek faces challenges common to many large language models, including data security, intellectual property concerns, algorithmic bias, and defining legal liability. A key issue is model hallucination, where AI generates plausible but incorrect or fabricated legal information. This raises concerns about the reliability of AI-generated content in legal practice.
Wang explains that hallucinations occur because models predict text based on probability rather than retrieving verified facts. Simply feeding more data to fix this is unlikely to eliminate hallucinations. While restricting AI responses to specific databases could help, such solutions lack operational feasibility at present.
The root cause lies in the architecture of large models like DeepSeek, which generate responses by recombining pre-training knowledge and user input through deep learning. Even with added data, accuracy improvements are limited without extensive modifications, which require significant resources and pose implementation challenges.
Yingke emphasizes that ensuring accuracy in legal AI requires a multi-pronged approach involving algorithm design, risk assessment, and data monitoring. They stress the importance of authoritative data sources and professional legal literature. Yingke cleans and annotates proprietary data meticulously—marking legal provisions, case types, and dispute focuses—to enhance model learning. Additionally, embedding legal domain logical rules allows the AI to apply rule-based reasoning, improving consistency and accuracy.
For legal professionals and product developers looking to adopt AI responsibly, understanding these limitations and implementing thorough data governance and domain-specific training is essential. To explore AI courses tailored for legal and product development professionals, visit Complete AI Training.
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