大语言模型的技术应用前景与风险挑战

Technology application prospects and risk challenges of large language models

针对大语言模型(LLM)技术的快速发展,剖析它的技术应用前景和风险挑战,对通用人工智能(AGI)的发展和治理有重要参考价值。首先,以Multi-BERT(Multilingual Bidirectional Encoder Representations from Transformers)、GPT(Generative Pre-trained Transformer)和ChatGPT(Chat Generative Pre-Trained Transformer)等语言模型为代表,综述LLM的发展脉络、核心技术和评估体系;其次,分析LLM现存的技术局限和安全风险;最后,提出LLM在技术上改进、政策上跟进的建议。分析指出作为发展阶段的LLM,现有模型存在非真实性及偏见性输出、实时自主学习能力欠缺,算力需求庞大,对数据质量和数量依赖性强,语言风格单一;存在数据隐私、信息安全和伦理等方面的安全风险。未来发展可从技术上继续改进,从“大规模”转向“轻量化”、从“单模态”走向“多模态”、从“通用”迈入“垂类”;从政策上实时跟进,实施有针对性的监管措施,规范应用和发展。

In view of the rapid development of Large Language Model(LLM)technology,a comprehensive analysis was conducted on its technical application prospects and risk challenges which has great reference value for the development and governance of Artificial General Intelligence(AGI).Firstly,with representative language models such as Multi-BERT(Multilingual Bidirectional Encoder Representations from Transformer),GPT(Generative Pre-trained Transformer)and ChatGPT(Chat Generative Pre-trained Transformer)as examples,the development process,key technologies and evaluation systems of LLM were reviewed.Then,a detailed analysis of LLM on technical limitations and security risks was conducted.Finally,suggestions were put forward for technical improvement and policy follow-up of the LLM.The analysis indicates that at a developing status,the current LLMs still produce non-truthful and biased output,lack real-time autonomous learning ability,require huge computing power,highly rely on data quality and quantity,and tend towards monotonous language style.They have security risks related to data privacy,information security,ethics,and other aspects.Their future developments can continue to improve technically,from“large-scale”to“lightweight”,from“single-modal”to“multimodal”,from“general-purpose”to“vertical”;for real-time follow-up in policy,their applications and developments should be regulated by targeted regulatory measures.