👉 How does an LLM differ from traditional AI models and other language models?
The difference between LLMs and "traditional" AI models lies in the way they are trained and the types of tasks they can perform. Traditional AI models are often designed for specific tasks, such as text classification or entity extraction. They are usually trained on specific annotated datasets and are limited to the specific task for which they were trained.
LLMs, on the other hand, can perform a wide range of natural language processing (NLP) tasks, including (but not limited to) text generation, text classification, sentiment analysis, machine translation, and more.
LLMs are trained on gigantic datasets that can include billions of words or texts. They require large amounts of data and significant computational resources for training. Typically, working with such models requires specialized hardware and software, as well as considerable technical expertise. Due to the size of the models and training data, the quality of the output is very high and, especially when generating texts, often indistinguishable from human-generated information.
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👉 What are the application areas for Large Language Model (LLM)?
LLMs can be used in a variety of ways. Among other things, LLMs can provide support in the following cases:
> LLMs can be used to analyze large amounts of text, recognize topics, and summarize long texts and articles.
> LLMs can serve as the basis for interactive chatbots and virtual assistants that can conduct human-like conversations.
> LLMs can serve as the basis for intelligent question-answering systems that can answer complex questions and provide solutions to specific problems.
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👉 What are the advantages of combining LLMs with a search engine?
The delivery of false information (hallucination) is a known risk when using LLMs.
If the LLM is integrated into a search engine such as iFinder, this risk is minimized. The summaries and answers are based on the facts from the hit lists, which are determined by the search engine and not generated by the language model. The hit lists are also rights-checked, which means that the summaries and answers only contain information for which the respective user is authorized. This also prevents data protection violations.
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👉 LLMs as SaaS or On-Premises?
The decision as to whether an LLM is operated on your own in-house servers (on-premises) or rented as a cloud solution as SaaS (Software-as-a-Service) depends on various factors.
In favor of an on-premises solution is the control that you as a company or authority have over the entire infrastructure and data. All data and processes are managed internally to minimize potential security concerns and meet compliance requirements.
What speaks in favor of a SaaS solution is that you don't have to set up the hardware infrastructure for operating the LLMs yourself.
With iFinder, we can process your data both on-premises and in the cloud with LLMs.
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