Benefits & Limitations of Using Large Language Models (LLMs)

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.

An Overview of Large Language Models (LLMs)

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:

1. Proprietary large language models:

They require a license to run, and this license lays down the usage restrictions for the LLM model used.

2. Open-source large language models:

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.

• Speech recognition: Large Language Models work well in speech recognition, initiating natural interactions with customers across various platforms.
• Language Translation: The models make language translation tasks easy, accurate, and faster, thus improving quality whenever used.
• Virtual assistants and Chatbots: Large Language Models run chatbots and virtual assistants. They provide accurate and helpful interactions, mimicking human interactions.
• Virtual collaboration: The models help in improving employee productivity and effectiveness. They work on streamlining routine tasks, such as automatic documentation, classifying documents, segmenting data, data mining, and more.

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.

Benefits of LLMs

1. Transparency & Flexibility

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.

2. Cost-Effective

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.

3. Legal and Compliance

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.

4. Custom Functionality

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.

5. Easy code generation

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.

6. Content filtering

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.

Limitations of LLMs for Businesses

• An incomplete training data for LLM could be highly risky for businesses. This also includes inaccurate data that can predict false market trends for businesses, resulting in irrelevant information.
• There are certain security issues, including leaking PII and cybercriminals using LLM for malicious tasks like spamming, threats, and hacking. They also attempt to change the original programming of these models, causing irrelevant results.
• Issues of bias in AI models are very common. Businesses using LLMs should keep a watch if their models function with bias. To remove it, companies use trained data and monitor data wisely for LLM models.
• One of the key limitations of using the models is controlling their output. Data plays a key role here.

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:

• proper management of data
• continuous data monitoring
• Alignment with legal requirements
• Right addition of algorithms
• Mitigating Bias

Also Read: AI Language Models: Potential Risks and Limitations

Conclusion

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

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