By Anushree Bhattacharya - October 30, 2023 5 Mins Read
Large Language Models (LLMs) are revolutionizing the use of AI for businesses worldwide. However, companies should be aware of its use’s compelling benefits and dangerous limitations.
Today, automation is one of the most important tools that streamline business operations. One of the key players boosting automation is large language models (LLMs).
Natural language processing (NLP) made these models useful for generating human-like language and understanding intent-based data. However, like any other technology, LLMs have their benefits and limitations, which companies should know.
This article focuses on both aspects and spotlights how companies should use them wisely.
The models use artificial intelligence (AI), deep learning, and massive data sets from various sources like websites. They collect data to generate text, translate among different languages, and create creative content.
LLMs use two types of generative AI models:
They require a license to run, and this license lays down the usage restrictions for the LLM model used.
Open-source LLMs are universal, available for free access without any purpose, easy to modify, and distributed easily among businesses. The models use “open source” LLM codes accessible to the public.
LLM models work with a pre-training technique that involves training a vast amount of data. Upon training, the data learns various patterns or a set of patterns and structures of language. Ultimately, this process delivers natural language, called natural language processing.
Let’s explore the rising applications of the models in this section.
In sales, LLMs are also used for lead identification and generation, boosting personalization with messages and emails, gathering customer data insights, and assisting marketing and sales teams in initiating campaigns.
The benefits will help enterprises find more opportunities to use the models to serve various other business operations at scale.
LLMs are becoming widely available for businesses due to their transparency and flexibility features. The benefit is especially for enterprises that do not own machine learning software.
They can use open-source LLMs that provide transparency and flexibility regarding data and network usage. It reduces the risk of data leaks or unauthorized access.
Enterprises can also benefit from its functionality, data training, and methods of using those data.
Additionally, visibility into algorithms allows enterprises to gain more trust, assist in audits, and ensure ethical and legal compliance.
The models are more affordable for enterprises in the long term than proprietary LLMs, as they need no licensing fees.
However, the cost of operating an LLM includes the cloud or on-premises infrastructure costs, which are cited as affordable.
LLM models can assist in legal updating, contract analysis, compliance monitoring, and document review. They streamline legal processes, reduce the time spent on document analysis, and ensure regulatory compliance.
LLMs allow developers to customize the AI model, algorithms, and data interpretation capabilities according to business needs and operations. Training a custom model allows them to transform a universal solution into a business-specific tool.
With help from Ml experts and developers, enterprises can fine-tune the model and train it on their proprietary data. Then, they can use it to run applications for enterprise usage
This will allow the models to support specific tasks like personalized content creation to customer support and data extraction.
LLMs can be trained on existing codes and programming languages. However, business leaders should have the proper technology and software to generate relevant codes to complete tasks through LLMs.
LLMs are valuable for businesses in identifying and filtering out inappropriate or harmful content. This is a huge benefit in maintaining a safer online environment.
While these benefits have been a boon for many companies, there are some limitations to using LLM models.
Let us see the major challenges that enterprises face while using LLM models.
While these limitations are important to consider, they can be rectified and overcome with the help of methods, strategies, and techniques. Some of the techniques may include:
Despite the limitations, LLMs are one of the key requirements today for companies to boost their digital businesses.
The use of the models is more today as they have revolutionized the use of AI to generate more accurate and effective applications for customers to interact naturally.
Companies planning to use LLMs for their businesses should be aware that according to a report, Natural Language Processing-Worldwide, by Statista
Anushree Bhattacharya is a Senior Editor with Ondot Media, where she covers stories on B2B business strategy, thought leadership, and corporate technology culture. She is a quality-oriented professional writer with eight years of experience. She has been curating content for the B2B industry, and her writing style is inclined toward how businesses want to perceive information about emerging digital transformations and technology developments. Anushree blends the best information on trending digital transformations, technology-driven stories, and SEO-optimized content. Anushree is proficient in technology journalism and curates information-driven stories about enterprise tech for EnterpriseTalk publication.
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