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.
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.
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.
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.
To begin, each example we use falls into one of these classes.
Now that we've touched on the classes, let's go through this list of large language models.
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.
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 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.
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.
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.
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.
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.
Large language models offer several advantages on a variety of fronts.
Large language models solve many business problems, but they may also pose some of their own challenges.
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.