As a disruptive innovation, ChatGPT has become a popular smart application all over the world.
Since the explosion of ChatGPT, the domestic technology circle has begun to make frequent efforts, and many technology and Internet companies have expressed that they will develop China's localized ChatGPT.
Take Baidu as an example. On March 16, Baidu launched a new generation of knowledge-enhanced large language model - Wenxin Yiyan. At the press conference, Baidu CEO Robin Li demonstrated Wenxinyiyan's comprehensive capabilities in five usage scenarios: literary creation, commercial copywriting, mathematical calculation, Chinese understanding, and multimodal generation. Baidu Wenxin Yiyan is positioned as an artificial intelligence-based empowerment platform, which will help the intelligent transformation of various industries such as finance, energy, media, and government affairs.
Wen Xin Yi Yan is currently the only model that can directly perform "Wen Sheng Diagram" and has multi-modal generation capabilities, including the ability to generate pictures, generate voices (including dialects), and generate videos. They have good expressive ability in literary creation such as poetry, but poor performance in answering mathematics and code questions.
At present, there is still a big gap between Wenxin Yiyan and ChatGPT. Regarding everyone's doubts and opinions, Li Yanhong said, "Wen Xin Yi Yan is not perfect. The reason why it is released now is because of the strong demand in the market. Once the big language model is released, it will continue to receive real feedback from customers, and the iteration speed will be very fast. Hurry up." Wen Xin Yiyan will continue to learn and correct mistakes.
The biggest feature of the large language model behind the ChatGPT large model and Wenxinyiyan is the reinforcement learning through human feedback. In short, it uses manual annotation to write answers, and gives different feedback to the model according to the results. If the answer is correct, positive feedback will be given. If the answer is wrong, the model will iterate itself and continuously tune until the answer is correct. Such a large-scale model has particularly high requirements on data quality and data category diversity, and requires a large amount of high-quality labeled data for support.
Jinglianwen Technology is a leading company in the AI basic data industry. It has a data annotation team with thousands of employees and rich experience in image and text annotation. It can provide images and NLP for the ChatGPT model and Wenxin Yiyan's big language model. Relevant data collection and data labeling services, and quickly deploy labelers with relevant experience according to customer needs.
Currently, the data available for large language model training covers professional knowledge from all walks of life, and the data sources are diverse, in different formats, and widely distributed. Such data cannot be used directly and needs to be cleaned, rewritten, and marked before it can be used. JLW Technology has rich expert resources, including experts in the fields of code, medicine, advanced mathematics, world knowledge, translation, literary creation, etc., who can label data information in vertical fields, so as to ensure data quality and meet current labeling needs.
For data customization labeling services, JLW Technology has an advanced data labeling platform and mature labeling, review, and quality inspection mechanisms, supporting computer vision: semantic segmentation, rectangular box labeling, polygon labeling, key point labeling, 3D cube labeling, 2D3D Integrate labeling, target tracking, attribute discrimination and other types of data labeling; support natural language processing: text cleaning, OCR transcription, sentiment analysis, part-of-speech tagging, sentence writing, intent matching, text judgment, text matching, text information extraction, NLU sentences Multi-type data annotation such as generalization and machine translation.
The products provided by Jinglianwen Technology are full-chain AI data services, from data collection, cleaning, labeling, to the whole process of on-site, one-stop AI data services for vertical field data solutions, which meet the needs of various application scenarios. To meet the needs of data collection and labeling business, assist artificial intelligence companies to solve the corresponding problems in the data collection and labeling link in the entire artificial intelligence chain, promote the application of artificial intelligence in more scenarios, and build a complete AI data ecology.
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