NATURAL LANGUAGE PROCESSING
Overview
In recent years, breakthroughs in deep learning, large language models (LLMs), and generative artificial intelligence (Generative AI) have injected new momentum into the NLP domain. TL continues to focus on applications of understanding and generating local languages and cultures, aiming to enhance the ability of computers to read, comprehend, and analyze vast amounts of textual information while integrating generative AI technologies to achieve collaborative creation and development with humans.Furthermore, we have developed conversational AI systems with multi-turn dialogue capabilities, knowledge reasoning, and question-answering functionalities. These technologies are applied in areas such as intelligent customer service, virtual assistants, conversational robots, as well as text analysis and generation for innovative applications. Looking ahead, we will continue to focus on the latest technological trends, driving innovation and adoption of intelligent industrial solutions to infuse more value into local businesses and societal development through AI empowerment.
NATURAL LANGUAGE PROCESSING
CORE TECHNOLOGY
- Text Understanding and Generation
- Conversational AI
- Customer Service Agent Assistant
Text Understanding and Generation
Conversational AI
Customer Service Agent Assistant
NATURAL LANGUAGE PROCESSING
Application Status
Text Understanding and Generation：We master the key technologies for building domain-specific large language models (LLMs). By leveraging the general capabilities of open-source LLMs, we prepare data from specialized fields such as law, public opinion, and customer service, and train them into "domain-specific LLMs" that provide accurate and reliable professional advice and analysis. We are committed to providing tailored solutions for domain-specific business needs, suitable for business scenarios involving cloud and edge services. Our research and development results are applied to legal document generation, online public opinion analysis, and customer service dialogue analysis, helping users reduce the time spent on manual data and document organization, speeding up the understanding and summarization of key points, and enhancing human-machine collaboration efficiency.
Conversational AI：Development of key core technologies for simulating human-like voice interaction. Based on Agentic AI, it achieves capabilities in task planning, tool invocation, and conversational interaction, enabling users to naturally communicate with AI chatbots via voice or text to complete specific tasks. The technological achievements are applied to scenarios such as restaurant reservations, smart receptionists, appointment scheduling assistants, issue reporting, and satisfaction surveys.
Customer Service Agent Assistant:By developing our own GPT and semantic retrieval technologies, the system automatically identifies relevant content from conversation history and instantly generates suggested replies to help customer service agents respond to inquiries. Leveraging open-source large language models and retrieval-augmented generation, we establish an on-premises, enterprise-grade knowledge Q&A service. This reduces the time agents spend manually searching for answers and significantly boosts their efficiency in handling customer questions.