Large language models use NLP and deep learning technologies to deliver personalized and contextually relevant output for the given input. LLMs are powerful, robust, and useful to an enterprise. Here, we’ll discuss the benefits, challenges, applications, and examples of LLMs.
Artificial intelligence is growing leaps and bounds in recent times. Generative AI brought a revolution and disrupted the global industry. The top brands are following suit by investing heavily in AI and large language models to develop customized applications like ChatGPT.
According to a Verta, Inc. survey, 63% of business organizations plan to continue or increase their budgets for AI adoption. Based on a report by Juniper Research, ML spending has increased by 230% between 2019 and 2023. Large Language models are being extensively researched and developed by universities and leading multinational brands in the international world.
While there’s no denying the heavy expenses required to implement LLMs in an enterprise, it cannot be ignored either. LLMs are proving to be beneficial on many fronts. From R&D to customer service, large language models can be used for a variety of tasks. In this blog, we’ll find out everything we know to know about AI large language models.
A large language model is typically an AI algorithm that uses artificial neural networks to process and understand inputs provided in the human language or text. The algorithms use self-supervised learning techniques by analyzing massive data in various formats and understanding the patterns, context, etc., to provide a relevant output as the answer.
LLMs can perform tasks like text generation, image generation (from text), audio-visual media generation, translating text, summarizing input, identifying errors in code, etc., depending on how and why it has been developed. The models can converse with humans and provide human-like answers.
Large language models essentially use deep learning and natural language processing technologies to understand complex entity relationships and generate output that is semantically and contextually correct. However, developing an LLM from scratch is cost-intensive and time-consuming. Large Language Model consulting companies work with open-source LLMs and train them with the client’s proprietary data to fine-tune the algorithm as per the business requirements. Enterprises can adopt LLM applications in a quick time and gain a competitive advantage.
An LLM has a highly complicated architecture with various components, technologies, and connections. However, the following parts are important in building a large language model for a transformer-based architecture:
The input text is transformed into individual words and sub-words in a process called tokenization. These tokens are embedded in a continuous vector representation. The semantic and syntactic information of the input is captured here.
This part deals with providing the position of each token based on the input. This ensures that the model understands the input in sequential order to retain its meaning and intent.
Encoder analysis is based on the neural network technique. An LLM will have multiple encoder layers. These are the core of the transformer architecture and have two stages – self-attention mechanism (identifying the importance of tokens based on attention scores) and feed-forward neural network (capturing interactions between the tokens).
Not all LLMs have a decoder layer. However, the decoder enables autoregressive generation for the model to generate the output based on the tokens.
Multi-head attention is where several self-attention mechanisms are run simultaneously to understand all possible relationships between the tokens. This allows the model to interpret the input text in multiple ways (useful when the text is vague).
Applied to each layer in the LLM, this part stabilizes the learning process of the algorithm and makes the model more effective in generating a more generalized output for various inputs.
The output layers change from one LLM to another as they depend on the type of application you want to build.
Now that you know how large language models work, let’s look at the advantages of implementing LLMs in an enterprise.
Large language models can be used in different departments for varied tasks. You can fine-tune the model with different datasets and change the output layers to deliver the expected results. LLMs can be used for numerous use cases. Businesses can develop more LLMs based on the core model and add more layers to expand it and use the applications across the enterprise. Over time, the LLMs can be adopted throughout the organization and integrated with the existing systems.
Even though LLMs are yet to reach their full potential, they are already flexible and versatile. You can use an LLM application for content generation, media generation (image, audio, video, etc.), classification, recognition, innovation, and many other tasks. Furthermore, the models can process input of any size (from a single line to hundreds of pages of text). You can deploy the models in each department and assign different tasks to save time for your employees.
Large language models can be expanded as the business grows. You don’t have to limit the role of LLMs in your enterprise as the business volume increases. The applications can be scaled to accommodate the changing requirements. They also can be upgraded with the latest technologies and datasets to continue providing accurate and relevant results. LLMs are easy to train as they can read and process unstructured and unlabeled data. There’s no need to spend additional resources on labeling data. However, low data quality can lead to inaccurate output and inefficient applications.
LLMs are robust, powerful, and highly efficient. They can generate responses in near-real-time and have low latency. Using an LLM application saves time for employees. It allows them to use the results right away and complete their tasks. For example, an employee doesn’t have to read dozens of pages to understand the content. They can use LLMs to summarize the information and read only the important points that too in less than a handful of minutes.
Large language models provide accurate output over time. As the models continue training on high-quality data, the generative AI algorithm will learn from the feedback to provide more relevant and contextually correct output. The transformer model of LLM delivers better results as you add more parameters and datasets when training. However, you should ensure that AI engineers monitor the LLM during the initial days to detect bias and errors and make the necessary corrections for accurate results.
Personalization has become mandatory to ensure customer experience and stay ahead of competitors. LLM applications like ChatGPT, Google Bard, Bing, etc., offer personalized output through the chats by processing the users’ input. LLMs can personalize results for customers and employees, depending on where, how, and why they are used. Chatbots and virtual assistants built on large language models use NLP to understand the meaning, intent, and context behind the input text to provide a relevant result exclusive to the user.
While large language models have many benefits, they also come with different challenges and complications. You can overcome the challenges by working with AI experts and LLM consulting providers to develop, deploy, and integrate large language models in your business. Let’s look at the core challenges to deal with when adopting LLMs.
Biased data is the biggest challenge and risk when developing LLMs. Since the models are trained on massive datasets, they learn and adapt from what’s provided. If the training data has an implicit bias against certain genders, races, or ethnic communities, the algorithm will project the same bias when delivering the output. This leads to skewed and erroneous results. Toxic language, slurs, racial jokes, demeaning certain religions, etc., will be a part of the output.
Every LLM has a context window or memory capacity. That means it cannot accept beyond a certain number of tokens as input. For example, ChatGPT has 2048 as the limit for input tokens. If the text entered goes beyond this value, the algorithm cannot make sense of the input. It leads to failed entry or nil output since the model cannot work with input beyond its acceptable capacity.
The main reason many businesses hesitate to adopt large language models is the high cost of investment. You need to spend huge amounts to develop and train the model and then continue allocating money for maintenance and system upgrades. Furthermore, if the business has outdated IT infrastructure, you should have a greater budget to digitalize the systems and processes to make them compatible with LLMs.
Developing and using a large language model requires extensive resources, which can adversely affect the environment. Many researchers are working on this to reduce the side effects on nature and make LLMs eco-friendly. Furthermore, LLMs can automate many tasks and complete large processes in lesser time. So they might be a better option in the long run.
Glitch tokens are prompts maliciously designed to make LLMs malfunction and deliver wrong or incorrect results. A few groups on platforms like Reddit have been experimenting with malicious codes and input commands to make ChatGPT collapse and kill itself. Such glitch tokens can damage the algorithm and cause heavy losses.
It comes as no surprise that LLMs are hard to troubleshoot. The models work with multiple components, technologies, databases, and parameters. It can be exhausting to correctly identify the problem resulting in certain erroneous outputs. Many AI engineers need to work together to troubleshoot an LLM.
Large language models came into popularity by powering chatbots and AI virtual assistants. Businesses dealing with B2C and B2B audiences can adopt LLM applications to empower their customer service department and enhance customer experience. Retail, eCommerce, and service sectors can largely benefit from this.
Search engines can be supported by LLMs to provide better, more accurate, and direct results to users’ queries. In a way, ChatGPT, Google Bard, etc., perform the job of a search engine by collecting data from various sites and presenting it to the user in brief summaries.
Research scientists can use LLMs to study in detail proteins, molecules, DNA, RNA, etc., to enhance their studies and discover new elements. The models help analyze scientific papers and speed up research work.
Software developers don’t have to spend endless hours writing code and executing it to detect errors. Large language models can do it on their behalf and complete the task in a fraction of the actual time taken. The models can also identify errors and make corrections in the code.
Sales teams can use LLMs to analyze customer feedback and behavioral patterns to create personalized promotional campaigns for each segment. The content for marketing the brand can also be created using these applications.
Large language models are useful in fraud detection in the financial, banking, and insurance sectors. Retailers and eCommerce businesses can also invest in LLM applications to ensure greater customer protection and minimize losses due to false claims and fake transactions.
Legal teams can reduce their workload by using LLMs to paraphrase the laws and present them in simpler terms for employees and stakeholders to understand. Instead of manually summarizing the content, they can rely on LLM applications to complete the job in a few minutes.
Large language models are all set to showcase greater advancements in the industry as AI researchers are actively collaborating and developing newer, better, and more powerful models for enterprise use. Hire large language model consulting services to adopt the latest AI technology in your business and streamline your internal and external processes. From manufacturing to logistics and customer support, LLMs can help you improve results, achieve goals, and increase ROI.