[Series preview 👀]
โ The difference between LLM and sLLM, easy to understand with PLATEER's POLAR (→ we're here now!)
โก E-commerce specialized 'X2BEE AI', this is what happens when it's loaded!
ChatGPT, LLM, and sLLM
ChatGPT is familiar, but LLM (Large Language Model) is unfamiliar? ChatGPT, which you are using comfortably these days, is a representative example of LLM. LLM is a deep learning model that understands human languages to derive results. In addition to ChatGPT, there are various LLMs such as Google's Gemini and Meta's LLaMA. Among the many LLMs, ChatGPT is the most famous and commercialized LLM. LLM is pre-trained based on vast amount of text data on the Internet and is used in various tasks such as text generation, translation, and summary.
However, this LLM also has some limitations. First, LLM tends to degrade in tasks that require domain knowledge (1), i.e., in-depth knowledge of a specific field. This is because LLM has learned data that encompasses various domains, but has not sufficiently dealt with data that is specific to a specific field. Second, LLM requires a lot of computational resources and energy to re-learn. The reason why small and medium-sized companies such as startups are burdened with developing or utilizing LLM themselves is that they have to invest a lot of money and resources.
To overcome this limitation, the smaller Large Language Model(sLLM) emerged. Based on LLM's general language processing capabilities, sLLM is a model designed to perform more deeply and accurately in a specific field. Certain domains such as healthcare, law, finance, e-commerce require higher accuracy and precision than general language models. Because sLLM is fine-tuned dataset with expertise in the field, it can understand and appropriately utilize specific terms and patterns! In addition, since sLLM utilizes fewer variables than LLM, it also consumes less computational resources. It provides high performance with less power and memory. In addition, it has a great advantage that it can be advanced to suit specific industries and types of work, and can be customized to accurately respond to the specific needs of the company.
LLM vs sLLM, to understand easily and clearly
If LLM that can answer questions in various fields is called 'all-round dictionary', sLLM can be compared to 'professional books' that have deep expertise in a specific field. I will explain how LLM and sLLM are different from each other and easily by substituting 'POLAR's Optimized sLLM and application with Reliability', an e-commerce-only sLLM developed by PLATEER.
| |
LLM |
sLLM |
POLAR |
| Learning Data |
vast amounts of data on a variety of topics |
Additional learning of specific domain data |
High-quality e-commerce data |
| Purpose |
general-purpose |
specialized |
E-commerce only |
| Compute Resources |
Mostly cloud-based operations |
Operate in a lightweight environment |
Enterprise internal server operations |
| Security |
Security concerns due to possible data externally exposure |
Learn based on security requirements
Operations in closed environments
|
Sensitive data can be safely processed in on-premises format!
|
| Use case |
For translation, summary, coding, and Q&A in almost all industries
|
Specialized and specialized features in a specific area |
Equipped with features to improve the productivity of shopping mall operations, such as customer consultation, marketing copy creation, and product search optimization |
| Customer Experience |
less accurate and less reliable answers to questions about specific areas
|
Customizable Experience |
Provide the best customer experience with accurate and professional answers related to shopping malls |
As shown above, LLM and sLLM differ in terms of the purpose of use, resources required, and security. sLLM has higher expertise and accuracy in a specific field compared to LLM, and is good to operate in an environment where security is important. In particular, POLAR, PLATEER's sLLM, is designed to safely process sensitive data such as customer information while improving the productivity of a company with various functions necessary for its mall operation based on high-quality data specialized in e-commerce.
PLATEER's POLAR on X2BEE
PLATEER's POLAR is a language model unique to PLATEER that was created after about two years of research and development. PLATEER has been built on the industry's expertise and data analysis capabilities that major companies have developed and operated e-commerce platforms over the past 20 years. This model is strong in understanding and handling complex language contexts that occur in shopping mall environments. You can utilize POLAR's capabilities in various situations such as product recommendations, customer inquiries, and personalized shopping experiences!
PLATEER has installed POLAR in its e-commerce platform solution, X2BEE, and introduced it under the name "X2BAI." X2B is already being used by many companies to build their own malls. With the recent addition of POLAR-based XTOB AI, innovative features have been added that can be practically used by shopping mall customers and operators. Are you curious to know what kind of shopping mall will look like with XTOB AI? Also, what kind of features will be advanced and newly released in 2025? Find out more in the following two-part content!
📌 Part 2 content: scheduled to be uploaded on February 11 (Tue)
📌 [Experiencing X2BEE AI] Take a look at the page and test it yourself (Click!)
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