Since the beginning of the year, the heat wave of ChatGPT has brought a national technological carnival, and at the same time opened up the industry's imagination for the development of NLP, and opened the prelude to the rapid development of the large language model industry and the generative AI industry.
In overseas markets, giants such as OpenAI, Microsoft, Google, and Meta are actively vying for the layout of ChatGPT. In the Chinese market, Baidu, Ali, Huawei, SenseTime, JD.com, HKUST Xunfei, Tencent, 360, ByteDance, Kunlun Wanwei, etc. Leading manufacturers are also rushing to announce the development or release of large language model products.
In March, Baidu launched Wenxin Yiyan, an application that benchmarks against Chat GPT; on April 9, 360 officially announced the landing search scene of the "360 Smart Brain" developed based on the 360GPT large model; on April 10, SenseTime released "Daily New SenseNova" large-scale model system; on the same day, Kunlun Wanwei announced that it will soon launch the "Tiangong" large-scale model; Series of AI large models"...the domestic market presents a thriving scene.
As a generative AI, ChatGPT subverts the inherent human-computer interaction method by using natural language interaction. Make it possible for everyone to solve problems by commanding the computer. Everyone can get things done with productivity tools, conversation engines, personal assistants, and more.
Before the emergence of ChatGPT, conversational AI products such as text robots, voice robots, and multimodal digital humans generally had problems such as imperfect knowledge structure, only answering simple questions, and insufficient understanding of semantics and emotions. The user's interactive experience is reduced. Combining conversational AI with large language model products is equivalent to installing a brain richer in human knowledge, wisdom, and emotion for the dialogue system, which can improve the pain points of previous conversational AI products, improve product functions, and add new product selling points.
Of course, ChatGPT still has many shortcomings, such as poor fact retrieval and mathematical calculation, and it is difficult to achieve some real-time and dynamic tasks, especially the Chinese corpus, which has become an insurmountable barrier for ChatGPT. Improving performance requires continuous reinforcement learning with human feedback.
The ChatGPT large language model has very high requirements for data quality and data category diversity. It is necessary to manually write the answers based on the sample data, then mark the classification and quality of the answers, and finally sort the multiple answer outputs given by the model, so that the model can be better consistent with human instructions. The quality and diversity of data becomes the key to model optimization.
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