2023年中国大模型行研能力市场探析：大模型底层助力，行研智慧前行

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The report highlights significant advancements in China's large language model (LLM) sector since the launch of ChatGPT, with over 80 different pre-trained language models emerging in the region, involving top academic institutions and tech companies [6][52] - The report emphasizes the transformative impact of LLMs on industry research capabilities, enhancing report writing efficiency and quality through advanced natural language processing techniques [5][54] - The evaluation of 12 major LLMs was conducted to assess their capabilities in industry research, focusing on report writing, foundational model abilities, and industry understanding [48][54] Summary by Sections Industry Overview - Large language models are defined as deep learning-based natural language processing technologies that learn from vast text datasets, achieving human-like content generation capabilities [19][20] - The evolution of LLMs has transitioned from rule-based systems to statistical machine learning, and now to self-supervised learning, marking a significant leap in human-computer interaction [26][29] Research Objectives - The report aims to understand the current development status of China's LLM industry, analyze its historical progression, explore its industrial value, and assess its application in industry research [9][10] Evaluation Methodology - The evaluation covered over 1,800 questions assessed by a team of 20 senior research analysts through a double-blind process, focusing on report writing ability, foundational model capabilities, and industry understanding [54][68] - The assessment framework includes a comprehensive 8-D methodology for evaluating industry research report writing logic and capabilities [68][71] Industry Understanding - The report includes insights from over 5,000 industry reports across 14 major sectors, evaluating the models' understanding and output capabilities in specific industry contexts [78][80] - The evaluation process involved detailed questions related to industry definitions, classifications, characteristics, and market dynamics [54][75] Challenges in Traditional Research - Traditional industry research faces significant challenges, including outdated tools, difficulties in knowledge transfer, and complex information sourcing, which hinder efficiency and innovation [41][45] - The report advocates for digital industry research solutions that leverage LLMs to overcome these challenges and enhance research quality and efficiency [35][41]