What are Large Language Models (LLMs)? Examples Included

Large language models (LLMs) understand and generate human-like text. They learn from vast amounts of data and spot patterns in language so they understand the context and produce outcomes based on that information. You can use LLM software to write text, personalize messaging, or automate customer interactions.

Many businesses turn to artificial intelligence (AI) chatbots based on LLMs to automate real-time customer support. However, even with their advantages, LLMs don’t come solely with all sunshine and rainbows; they have some challenges.

This article takes a look at various use cases of LLMs, along with their benefits and current limitations.  

Why are large language models important?

LLMs can perform several tasks, including answering questions, summarizing text, translating languages, and writing codes. They’re flexible enough to transform how we create content and search for things online. 

They might produce errors in output sometimes, but that usually depends on their training. 

Large language models generally get trained on internet-sized datasets and can do multiple things with human-like creativity. Although these models aren’t perfect yet, they’re good enough to generate human-like content, amping up the productivity of many online creators. 

How do LLMs work?

Previous machine-learning models used numerical tables to represent words. However, they were yet to recognize relationships between words with similar meanings. For present-day LLMs, multi-dimensional vectors, or word embeddings, help overcome that limitation. Now words with the same contextual meaning are close to each other in the vector space. 

LLM encoders can understand the context behind words with similar meanings using word embeddings. Then, they apply their language knowledge with a decoder to generate unique outputs.

Full transformers have an encoder and a decoder. The former converts input into an intermediate representation, and the latter transforms the input into useful text.

Several transformer blocks make a transformer. They’re layers like self-attention, feed-forward, and normalization layers. They work together to understand the context of an input to predict the output. 

Transformers rely heavily on positional encoding and self-attention. Positional encoding allows words to be fed in a non-sequential fashion. It embeds the input order within a sentence. Self-attention assigns weight to every piece of data, like numbers of a birthday, to understand its relevance and relationship with other words. This provides context. 

As neural networks analyze volumes of data, they become more proficient at understanding the significance of inputs. For instance, pronouns like “it” are often ambiguous as they can relate to different nouns. In such cases, the model determines relevance based on words close to the pronoun.

How are LLMs trained?

Large language models use unsupervised learning for training to recognize patterns in unlabelled datasets. They undergo rigorous training with large textual datasets from GitHub, Wikipedia, and other informative, popular sites to understand relationships between words so they can produce desirable outputs.

They don’t need further training for specific tasks. These kinds of models are called foundation models. 

Foundation models use zero-shot learning. Simply put, they don’t require much instruction to generate text for diverse purposes. Other variations are one-shot or few-shot learnings. They all improve output quality for selective purposes when they’re fed with examples of correctly accomplishing tasks.

Large language models examples

To begin, each example we use falls into one of these classes. 

• Encoder-only is suitable for tasks that involve understanding language to perform classification or sentiment analysis. Bidirectional Encoder Representation from Transformers (BERT) is a popular example of an encoder-only LLM class.

• Decoder-only works for use cases where LLMs write content like stories or blogs. Generative Pretrained Transformer 3 (GPT-3) is a popular example of a decoder-only LLM class.
• Encoder-decoder helps with understanding and generating content. Text-to-Text Transformer (T5) is one example. 

Now that we’ve touched on the classes, let's go through this list of large language models.

• GPT-3 is OpenAI’s LLM decoder-only transformer. Common Crawl, Wikipedia, WebText2, Books1, and Books2 datasets contribute to training this model.
• GPT-3.5 upgrades GPT-3 with fine-tuning using reinforcement learning from human feedback. It powers OpenAI’s ChatGPT. 
• GPT-4 is rumored to have more than 170 trillion parameters. It’s a multimodal model that can generate text and images. It powers Microsoft Bing Search and might be integrated with Microsoft Office products.
• BERT, introduced by Google falls in the encoder-only LLM class. With 342 million parameters, it uses large data sets for pretraining and fine-tuning to perform specific tasks.
• Claude usually powers AI assistants with principles to produce useful and accurate output. It focuses on constitutional AI and is good for complex reasoning. 
• Language Model for Dialogue Applications (LAMDA) uses a decoder-only transformer model trained on a heavy text corpus.
• Large Language Model Meta AI (Llama) has 65 billion parameters and requires less computing power to use, test, and experiment. Parameters are settings that control how LLMs generate text. 

• Orca has 13 billion parameters and can run on a laptop. It delivers the same performance as GPT-4 with fewer parameters.

• Pathways Language Model (Palm) works with 540 billion parameters to accomplish reasoning tasks such as writing code, solving math equations, or answering questions. 
• Phi-1 has 1.3 billion parameters and represents a trend toward smaller LLMs trained on quality data.
• Cohere allows users to fine-tune it according to a company’s use case. Unlike OpenAI, Cohere isn’t tied to a single cloud. 
• Ernie works best with Mandarin, but it’s capable in other languages, too. Baidu’s LLM powers the Ernie 4.0 chatbot. 
• Falcon 40B is a decoder-only LLM trained on English data. It’s an open-source LLM developed by the Technology Innovation Institute.
• Galactica caters to the needs of scientists. Meta trained it on academic materials, including 48 million papers, lecture notes, textbooks, and websites. Similar to other models, it authoritatively produces inaccurate information. Since this domain has no margin for error, scientists deemed it unsafe.
• StableLM is an open-source language model available in 3 billion and 7 billion parameter models. 30, 64, and 175 billion models are in the works. 
• Vicuna 33B is an open-source LLM derived from Llama with 33 billion parameters. Although it’s smaller compared to GPT-4, it does well for its size.

LLM vs. generative AI

All large language models are a form of generative AI, but not all generative AI is an LLM. You can think of large language models as a text-generation part of generative AI. Generative AI caters to use cases beyond language generation, including music composition, image, and video production. 

GPT-3 and GPT-3.5 are LLMs that create text-based output. With more research and development around multimodal LLMs, GPT-4 can now take input in the form of text, visual, or audio to produce multimedia outputs. 

Generative AI focuses on revolutionizing the industry and changing how we accomplish 3D modeling or create voice assistants. LLMs' focus is largely on text-based outputs, but it might play a significant role in other uses of generative AI in the foreseeable future.

LLM applications

Large language models have made various business functions more efficient. Whether for marketers, engineers, or customer support, LLMs have something for everyone. Let’s see how people across industries are using it.

Customer support

Customer support teams use LLMs that are based on customer data and sector-specific information. It lets agents focus on critical client issues, while engaging and supporting customers in real time.

Marketing

Sales and marketing professionals personalize or even translate their communication using  LLM applications based on audience demographics. 

Encoder-only LLMs are proficient in understanding customer sentiment. Sales teams can use them to hyper-personalize messages for the target audience and automate email writing to expedite follow-ups. 

Some LLM applications allow businesses to record and summarize conferencing calls to gain context faster than manually viewing or listening to the entire meeting. 

Product development and research

LLMs make it easier for researchers to retrieve collective knowledge stored across several repositories. They can use language learning models for various activities like hypothesis testing or predictive modeling to improve their outcomes.

With the rise of multimodal LLMs, product researchers can easily visualize design and make optimizations as required. 

Risk management and cybersecurity

Enterprises cannot do away with compliances in the modern market. LLMs help you proactively identify different types of risk and set mitigation strategies to protect your systems and networks against cyber attacks.

There’s no need to tackle paperwork related to risk assessment. LLMs do the heavy lifting of identifying anomalies or malicious patterns. Then, they warn compliance officers about the sketchy behavior and potential vulnerabilities.

On the cybersecurity side, LLMs simulate anomalies to train fraud detection systems. When these systems notice suspicious behavior, they instantly alert the concerned party. 

Supply chain management

With LLMs, supply chain managers can predict growing market demands, find good vendors, and analyze their spending to understand supplier performance. This gives a sign of increased supply. Generative AI helps these professionals 

Multimodal LLMs examine inventory and present their findings in text, audio, or visual formats. Users can easily create graphs and narratives with the capabilities of this large language model.

Benefits of large language models

Large language models offer several advantages on a variety of fronts.

• Improve continuously. The more LLMs learn, the better they become. After pretraining, you can use a few-shot prompting to help the model learn from inputs and produce more desirable outputs. 
• Don’t require many examples. LLMs learn quickly because they don’t need additional weight, resources, or training parameters. 
• Allow non-technical users automate monotonous tasks. LLMs can understand human language. Professionals can engineer their prompts in human language to set expectations from LLMs. They can use it to automate labor-intensive tasks. 
• Enable translation. LLMs learn different language structures through recurrent neural networks. This allows for easy cross-cultural communication and lets users personalize interactions in their customers’ local language. 
• Create summaries and deliver insights. You can quickly input comprehensive text or data and LLMs grasp context through summaries and analysis.

LLM challenges

Large language models solve many business problems, but they may also pose some of their own challenges.

• Require large datasets to train. Companies that intend to develop LLMs often struggle to get their hands on large enough datasets to effectively train their model. 

• Need niche technical experience. To develop LLMs, businesses need engineers and architects with a remarkable understanding of deep learning workflows and transform networks.

• Can make mistakes. If they’re trained on biased data, LLMs can produce biased outputs. They might even raise unethical or misleading content. 

• Have to have robust privacy measures. Large language models can struggle with data privacy, as working with sensitive information is tricky. 

• Are susceptible to hackers. Some malicious users design prompts to disrupt an LLM's functionality. These are known as glitch tokens and you need strong security to protect yourself against them.

Toward improved accuracy

As LLMs train with quality datasets, the outcomes you see will improve in accuracy and authenticity. One day, they could independently solve tasks for desired business outcomes. Many speculate how these models will impact the job market. 

But it’s too early to predict. LLMs will become a part of the workflow, but whether they will replace humans is still debatable. 

Learn more about unsupervised learning to understand the training mechanism behind LLMs.