Deciphering the 'myth' of large-scale models, cloud measurement data publishing industry AI large model data solution

Large models have the characteristics of good effectiveness, strong generalization, and standardized research and development processes. They have become an important direction for the development of artificial intelligence and bring new opportunities for the further development of artificial intelligence. This is information obtained from China Economic Weekly-Economic Network News

At present, the development of large-scale models is showing a flourishing trend and deeply empowering all walks of life, but it still faces many challenges in the industrialization process. Among them, how to efficiently obtain and effectively use vertical industry data is the key

At the 2023 China International Fair for Trade in Services, Cloud Measurement Data combined its rich experience and technology accumulation in the fields of intelligent driving, smart finance, AIOT, e-commerce and other fields to combine the "AI engineered data solution" released last year. "Solution" has been fully upgraded to provide full life cycle AI data solutions for large models in vertical industries, provide key support for the implementation of large model applications, and help high-quality development of large models in the industry.

Cracking the “illusion” of large models requires high-quality data

The development of large models is inseparable from the comprehensive support of algorithms, computing power and data. In the past two years, thanks to the rapid development of the three, large AI models have entered explosive growth. Among them, data is the key to promoting the high-quality development of large models.

"The pre-training of large models has particularly high requirements on data. It must be cleaned, annotated, and marked in the early stage. However, data training around thousands of industries also presents many problems and challenges in data supply." Shanghai Data Wei Zhilin, deputy general manager of the exchange, mentioned in a media interview.

Recently, major technology companies have frequently mentioned the "illusion" phenomenon of large models. The so-called "illusion" of large models means that the generated model text is incorrect, meaningless or unreal. People often call it "serious nonsense"

The emergence of the "illusion" problem is related to the core technical principle of large-scale models, that is, the next mark prediction under the Transformer architecture, that is, "predicting the next character". Therefore, increasing the quantity, quality, and diversity of data is critical to improving the performance of large models. Being data-centric has become the consensus of more and more people in the industry

Currently, major models are still unable to widen the huge gap in terms of computing power and algorithms, which makes "data" a key battle for companies to fight out the "Battle of 100 Models".

Deeply customized data solutions to help obtain high-value AI data

At the just-concluded 2023 Service Trade Fair results release, Cloud Test Data newly announced its AI data solutions, aiming to provide basic data sets and data for artificial intelligence companies and users through scenario-based data service industries. Annotation and data management tool chain to further improve algorithm accuracy

According to reports, this AI data solution can provide high-quality and efficient data for the entire life cycle of large industry models, from continuous pre-training, task fine-tuning, evaluation and joint testing to application release, helping vertical industry enterprises to better implement Large model related algorithm applications.

As a data service provider with rich data set accumulation and industry scenario data collection capabilities, Cloud Measurement Data can provide customers from all walks of life with customized data collection solutions to help them obtain high-value scenario data. data

When faced with fine-tuning tasks, we can provide relevant capability support for text-based task projects such as QA-instruct and prompt and multi-modal large models based on the characteristics of large models in actual application scenarios. After the fine-tuning is completed, we use cloud test data, accumulation of experts in vertical fields, and evaluation systems and services to help enterprises evaluate the actual effects of each vertical application field. At the same time, we also use the data annotation platform with the integrated data base as the core to reflow the difficult case data for cleaning and annotation to prepare for more efficient model tuning

In machine learning, natural language processing and other artificial intelligence fields, difficult example data refers to obstacles that are difficult to overcome during model training and testing and require special attention and resolution. Common difficult example data include spelling errors, grammatical errors, incomplete or redundant information, ambiguity and fuzziness, etc.

Currently, the in-depth partners of cloud measurement data cover multiple industries, including automobiles, security, mobile phones, home furnishings, finance, education, new retail, ecosystems, etc. Among them, it covers many Fortune 500 companies, university scientific research institutions, government agencies, leading AI companies and large Internet companies

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