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. Introduction
In the past two years, Large models of tongue ۔ This can be a minor exaggeration from me. Nevertheless, it is true that LLM is rapidly considered by many people that they demand AI or data -powered systems in the majority of real -world applications.
The purpose of this article is to bring back the conversation about LLMS to Earth. We will not only look for a wide range of use matters where LLM can increase the true value, but also their limits that face. It is very important to understand these limits because every challenge is not best dealt with with LLM, and in some scenarios, their use can also introduce unnecessary risks or complications.
. High use cases where LLMs increase real value
LLMS Natural Language Processing (NLP) is masterpiece that is designed to improve language understanding and language productive works. Below, the Arar is a list of some common understanding of language and breeding works, which requires the type of “language skills”, the basic (but not necessarily). For example, summarizing or translating the text usually involves a huge deal of language understanding, but eventually it also requires the language breed abilities to create output: a summary or translated version of the original input text.
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Although these work covers the most common cases of use for LLM, the debate has been summarized so far. Let’s look for some real -world situations where LLMs are the right tool for the task, which highlights the understanding of the language and/or race.
LLMS Natural Language Processing (NLP) is masterpiece that is designed to improve language understanding and language productive works.
!! Automatic Customer Support
It is a matter of use of a high demand in sectors like retail and e -commerce, where LLM can have a major impact. A text like a customer review or inquiry sent by a web form to understand and classify the user’s intentions (definitions, complaints, applications, etc.) can be analyzed. This specific task, especially the question answering the question, is best focused on the construction of the last one LLM -based virtual assistant, which is capable of understanding and answering the questions of a variety of users expressed in natural language.
!! Summary of the document
In areas such as law, scientific research, and to some extent, journalism, long and complex text documents, such as articles and reports, can be useful to include a comprehensive and readable summary that covers key insights and facts. Although this use of LLMs can significantly increase the performance of difficult use issues such as scientific literature reviews, it is important not to completely rely on the summary generated from LLM and also check the statistics that are more related to specific aspects or details.
!! Multi -linguistic communication
When used for translation, LLMS cross is a great tool to enable the linguistic understanding. They are useful for consumer opinion management in an e -commerce firm that works in many countries, provides personal support, and usually handle content in many languages. If properly trained on appropriate and diverse data, LLMS can also help translate potential local slags or phrases that cannot be considered at first glance.
!! The meaningful search and the question answer
When LLMs are integrated with recovery into collective generation systems that can get a deep understanding of the user’s inquiry, they can be used with great effectiveness to answer complex, open questions on the database or documents, which are directly aware of, and directly.
!! Generation of creative text
Last but least, LLM has amazing creativity to develop text with diverse style, structure and intentions. From the specific and charming product description and statement content with solid fluency and tone, to charming poems in many different styles, LLMS can create a wide range of creative text.
. When to use something else? LLM limits
Despite their great ability to understand different languages and their great ability to handle the work of the language of the language, which can often be very difficult, it is not realistic to consider them as a solution for all kinds of problems. Many use issues that have historically been resolved using traditional machine learning solutions-such as the rating, regression and forecasting system-is also given the best attention through the construction of a specific machine learning model that is related to the domain-related data to perform the target predictors.
Traditionally, other specific tasks resolved by early -generation AI systems, such as rule -based systems or models of logical reasoning, are still focused on such a traditional approach in some cases: less delayed decision -making and factual reasoning.
The following is a comprehensive list of cases where LLMS capabilities are limited, which highlights the right alternative approach to use.
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. Summary and wrapped
The LLMS scenario is Excel that requires a generation of creative text, extract key complex information from non -imposed text setting, and take advantage of the discussion assistant applications. However, their effectiveness is limited to predictive scenarios that call for high precision, real -time performance, domain logical reasoning, or access to specific, proprietary data.
Ivan Palomars Carcosa AI, Machine Learning, Deep Learning and LLMS is a leader, writer, speaker, and adviser. He trains and guides others to use AI in the real world.