Introduction

Large language models (LLMs) are text-generating, summarizing, and classification machine learning models that have been trained on enormous volumes of text data. A number of LLMs, including Google's PaLM 2, OpenAI's GPT-4, Cohere's Command model, and Anthropic's Claude, have shown they can produce language that is human-like, frequently with remarkable coherence and fluency. GPT-3 and BERT, which were trained on enormous volumes of text data from the internet and other sources, were the most well-known instances of huge language models prior to the introduction of ChatGPT. Natural language processing (NLP) applications that LLMs can typically handle include copywriting, content summary, chatbots, code development and debugging, question answering, and translation.

Large language models are, in essence, language prediction models. These models, often known as prompts, attempt to forecast the most likely word that will appear next based on the words that are supplied as input. Based on a statistical examination of all the "tokens" they have consumed during training—strings of characters that are concatenated to make words—these models produce text one word at a time. We will go through a few of the many features and uses for LLMs in this guide. .

Even though these models are now far more capable and readily available, many businesses are still unsure about how to appropriately implement them. According to the Refonte.AI Zeitgeist 2023 research, just 21% of respondents have generative models in production, despite the fact that the majority of respondents (60%) are experimenting with them or intend to work with them in the upcoming year. Adoption was hampered in many firms by a lack of knowledge, shifting corporate cultures, and outdated software and tools. In order to assist you better understand large language models and how to begin implementing them for your use cases, we prepared this guide.

Large Language Models: Why Are They Important?

With their many uses, large language models have transformed natural language processing. These models are changing the way we make things, see the world, and go about doing business. We can compose blogs, emails, and ad copy more rapidly and creatively when we use large language models. They make it possible for developers to write code more quickly and assist them in locating errors in big code bases. Additionally, developers don't need to have experience with machine learning to link their apps with LLMs using English-language prompts, which speeds up innovation. In order to swiftly comprehend the most important information from reports, news pieces, and corporate knowledge stores, large language models condense long-form content. At last, chatbots are fulfilling their potential to help companies increase customer satisfaction and streamline processes.

Large Language Models are more available to a wider audience than ever, as companies such as OpenAI, Anthropic, Google, Stability AI, and Cohere provide APIs or open-source the models to be used by the larger community. Additionally, the talent pool of machine learning engineers is growing and new roles such as "prompt engineer" are becoming popular.

Large language models are capable of generalizing to a broad range of tasks and styles because of the vast amount of data they have been trained on. These models can tackle problems of the same kind when provided an example of the problem. However, these models are not very good specialists right out of the box. Businesses need to fine-tune models on their own data in order to fully benefit from LLMs. Take a financial services company that wants to conduct investment research, for instance. Base models can only access old publicly accessible data, so while they can offer general information about stocks or other assets, they frequently cannot or will not provide investing advice. Alternatively, a fine-tuned model with access to private research reports and databases is able to provide unique investment insights that can lead to higher productivity and investment returns.

Effective implementation of Learning and Leadership Modules (LLMs) can facilitate employee empowerment, boost productivity, and improve customer satisfaction for firms. Now, let's examine these models' operation and appropriate application to optimize your company's gains.

Common Use Cases for Large Language Models

LLMs are capable of a wide variety of tasks, the most common of which we will outline here. We will also discuss some domain-specific tasks for a select few industries.

Classification and Content Moderation

Numerous natural language processing tasks, including classification tasks, can be handled by large language models. Assigning a given input to one or more predefined groups or classes is the process of classification. A model could be trained, for instance, to identify if a statement has a favorable or negative mood. Beyond sentiment analysis, LLMs can be used to identify the reasons why consumers contact (no more having to navigate lengthy phone menus to reach the right agent) and classify user comments according to its specific purpose, such as feature requests, problem reports, and UX ideas.

Another common use of LLM's categorization power is content moderation. Reporting users who upload offensive or harmful content is a frequent use case. LLM are incredibly flexible methods for content control since they can be easily adjusted to swiftly adjust to changing policies.

Classification: Classify each statement as "Bearish", "Bullish", or "Neutral"

Text Generation

Generating text that seems like it was written by a person is one of large language models' most remarkable features. Large language models can instantly generate well-written, cohesive prose on nearly any subject. They can be used for a wide range of tasks because to this capability, including automatically producing answers to consumer questions and even coming up with unique material for social media posts. Users have the option of specifying the tone of the response, ranging from lighthearted to formal, and of having it written in the same style as writers like Dale Carnegie or William Shakespeare.

Text Extraction

Important information can also be gleaned from unstructured text by using large language models. For more real-time use cases like contact center efficiency, including automatically parsing a customer's name and address without a structured input, this can be especially useful. Because they can distinguish crucial details from irrelevant material and comprehend the context of words and sentences, LLMs are especially skilled at text extraction.

Summarization

Text summarizing is another task that large language models are capable of performing. It involves condensing a given text and keeping its essential concepts and information. Financial analysts may find this helpful for summarizing the documents and evaluating financial statements, historical market data, and other proprietary data sources. Text extraction and text summarizing work extremely well together. These two characteristics can be combined to recover pertinent information from enormous corpuses of written data, such as unstructured text or knowledge stored in structured databases, and then summarize it for human consumption while properly attributing the source. This is especially useful for businesses or industries that produce a lot of written data.

Question Answering

Knowledge bases, papers, and user-supplied text can be mined by LLMs for information that addresses specific queries, such which product categories to choose or which assets yield the best profits. These models can provide very accurate answers when they are refined via RLHF and fine-tuned on domain-specific data. Think about an eCommerce chatbot, for instance

Software Programming

The way software developers write code is likewise being changed by these paradigms. Developers now have a strong tool to improve their efficiency and troubleshoot their code thanks to code-completion technologies like Github Copilot and OpenAI's codex. Programmers have two options: they can request custom functions or submit already-existing programs and ask the LLM to assist with debugging. As the context window size increases, these tools will be able to help analyze entire code bases as well.

General Assistants

Big language models can be used to create content, analyze data, and even aid in the design of new products. Swiftly handling and evaluating vast volumes of data can aid companies in improving decision-making, boosting worker efficiency, and maintaining a competitive edge.

A Brief History of Language Models

Knowing the background of natural language processing is crucial to putting the effects of these models into proper perspective. While the idea of using neural networks for natural language processing dates back to the 1980s, and the field of natural language processing (NLP) emerged in the 1940s following World War II, it wasn't until recently that the processing power provided by GPUs and the data required to train very large models became widely available. The two main paradigms of natural language processing from the 1950s to the 2010s were symbolic and statistical.

In the 1980s, recurrent neural networks, or RNNs, gained popularity. An artificial neural network (ANN) in its most basic form, an RNN can process sequential data, but it has trouble with long-term dependencies. LSTMs, a kind of RNN with a gating mechanism that helps it better handle long-term dependencies, were developed in 1997. The usage of LSTMs in numerous for-profit speech-to-text systems started to revolutionize speech recognition around 2007.

Throughout the early 2000s and 2010s, trends shifted to deep neural nets, leading to rapid improvement on the state of the art for NLP tasks. In 2017, the now-dominant transformer architecture was introduced to the world, changing the entire field of AI and machine learning. Transformers use an attention mechanism to process entire sequences at once, making them more computationally efficient and capable of handling complex contextual relationships in data. Compared to RNN and LSTM models, the transformer architecture is easier to parallelize, allowing training on larger datasets.

2018 was a seminal year in the development of language models built on this transformer architecture, with the release of both BERT from Google and the original GPT from OpenAI.

In 2018, one of the first large language models to achieve state-of-the-art outcomes on a variety of natural language processing tasks was BERT, or Bidirectional Encoder Representations from Transformers. Because BERT is a somewhat lightweight model that is economical to run in production, its categorization capabilities have made it frequently employed in business today. While BERT was considered state-of-the-art when it was originally introduced, more recent generative models like GPT-4 have since exceeded it in almost all benchmarks.

The GPT model family is different from BERT in that it is generative and has a much bigger scale, which allows it to outperform BERT on a variety of tasks. 2019 saw the introduction of GPT-2, while 2020 saw the announcement of GPT-3 and its widespread release in 2021. In 2021, Google unveiled LaMDA and released the open-source T5/FLAN (Fine-tuned Language Net) model, pushing the state of the art with extremely powerful models.

In 2022 the open-source BLOOM language model, the more powerful GPT-3 text-davinci-003, and ChatGPT were released, capturing headlines and catapulting LLMs to popular attention.In 2023, GPT-4 and Google's Bard chatbot were announced. Bard was originally running LaMDA, but Google has since replaced it with the more powerful PaLM 2 model.

Currently, there exist multiple competitive models such as Claude from Anthropic, the Command Model from Cohere, and StableLM from Stability AI. Over the coming years, we anticipate seeing these models continuing to advance and acquire additional features. Multimodal models will be able to process and react to images and videos in addition to text, with a cohesive comprehension of the connections between various modalities. Less hallucinations and increased dependability in interacting with tools and databases will be experienced by models. As a consequence, developer communities will mushroom around these models, bringing in a new wave of rapid innovation and productivity. Although we anticipate larger models, we also anticipate that in order to improve model performance, model builders will concentrate more on high-quality data.

Model Size and Performance

As LLMs have grown in size throughout time, so have their capabilities. Usually, the number of parameters (the number of values the model can vary as it learns) or the amount of the training dataset, expressed in tokens (word fragments), are used to calculate the size of the model.

• BERT (2018) was 3.7B tokens and 240 million parameters.
• GPT-2 (2019) was 9.5B tokens and 1.5 billion parameters.
• GPT-3 (2020) has 499B tokens and 175B parameters.
• PaLM (2022) was 780 Billion tokens and 540 billion parameters

Fine-Tuning Large Language Models

Fine-tuning is a process by which an LLM is adapted to specific tasks or domains by training it on a smaller, more targeted dataset. This can help the model better understand the nuances of the specific tasks or domains and improve its performance on those particular tasks. Through human evaluations on prompt distribution, OpenAI found that outputs from their 1.3B parameter InstructGPT model were preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Fine-tuned models perform better and are much less likely to respond with toxic content or hallucinate (make up information). The approach for fine-tuning these models included a wide array of different domains, though still a tiny subset compared to the entirety of internet data.

This idea of fine-tuning to improve task-specific performance also holds true for discrete domains, like a certain task or industry. Large language models (LLMs) are extremely beneficial for organizations when they are fine-tuned. A business that offers language translation services, for instance, could train an LLM to better comprehend the subtleties of a given language or industry, such insurance claims or legal paperwork. This knowledge enables the model to provide translations that are more fluid and accurate, improving client happiness and possibly even increasing revenue.

A company that creates product descriptions for e-commerce websites is another illustration. A more detailed and captivating description of a product might be produced by fine-tuning an LLM to comprehend its features and attributes. This could boost client engagement and sales. Businesses can enhance their performance across a range of jobs and domains by fine-tuning an LLM to fit their unique demands.

Fine-tuning an LLM generally consists of the following high-level process:

• Decide the task or domain you wish to fine-tune the model for. Examples include text summary, product descriptions, and language translation.
• Assemble a focused dataset that is pertinent to the domain or task that you wish to optimize the model for. This dataset should be sizable enough to provide the model plenty material for learning, but not so sizable as to make training take an inordinate amount of time. The minimum required quantity of training instances is a few hundred, and more data improves the quality of the model. Make sure the information relates to your industry and particular use case.
• To train the LLM on the smaller dataset, use a machine learning framework, library, or application such as Refonte.Ai Spellbook. The original data, or "input text," and the accompanying desired output—such as a summary or the text's classification into a set of predetermined categories—must be given to the model. It will then be left to learn from the data by making adjustments to its internal parameters.
• As the model trains, keep an eye on its performance and tweak the training procedure as needed to get better results. This can entail altering the model's learning rate, expanding or contracting the training dataset, or changing the architecture of the model.
• Examine the model's performance on the particular task or domain you configured it for after it has been trained. This will entail feeding text into the model and comparing the resultant output to what is intended.

Reinforcement Learning from Human Feedback (RLHF)

A technique for training machine learning models by requesting feedback from human users is called reinforcement learning from human feedback, or RLHF. Learning can be done more effectively with RLHF. As an alternative to trying to create a loss function that will make the model behave more like a person, RLHF involves training humans as active participants. Models produced by RLHF are better in line with human expectations, a common qualitative indicator of model performance.

RLHF-trained models like InstructGPT and ChatGPT offer the advantage of being more beneficial and goal-aligned overall. Compared to models trained using alternative techniques, these models are more adept at obeying commands and have a lower tendency to fabricate information (hallucinate). Furthermore, these models have performance comparable to that of conventional models, but at a much smaller size (InstructGPT has 1.3 billion parameters, whereas GPT-3 has 175 billion parameters).

LLM Prompt Engineering

The process of meticulously creating the input text, or "prompt," that is fed into an LLM is known as prompt engineering. One can manipulate the model's output and direct it to produce more desirable results by crafting a well-crafted prompt. Controlling model outputs is helpful for a number of tasks, including text generation, question answering, and sentence translation. An LLM may produce irrelevant, illogical, or otherwise unacceptable replies in the absence of quick engineering. Prompt engineering can be used to make sure the model maximizes its advanced capabilities and produces the intended outcome.

Although prompt engineering is still in its infancy, a new profession known as the "Prompt Engineer" is already taking shape. The person in charge of creating and constructing the input text, or "prompts," that are fed into large language models (LLMs) is called a prompt engineer. The capabilities of LLM as well as the particular duties and applications it will be utilized for must be thoroughly understood by them. In order to direct the model to produce the intended output, the prompt engineer must first be able to recognize the desired outcome and then carefully build prompts. In actuality, this could entail structuring the suggestion in a particular way, using precise words or phrases, or giving context or background information. The prompt engineer must be able to work closely with other team members and adapt to changing requirements, datasets, or models. Prompt engineering is critical in ensuring that LLMs are used effectively and generate the desired output.

Prompt Engineering for an LLM generally consists of the following high-level process:

• Decide the application or task—such as creating text, responding to inquiries, or summarizing reports—you wish to utilize the LLM for.
• Choose the precise output that you want the LLM to produce; this may be a written paragraph, a single classification value, or a series of lines of code.
• Create a prompt with great care to direct the LLM to produce the intended result. To make sure the terminology is understandable, be as descriptive as you can and include context or background information.
• Input the prompt into the LLM and see what results it produces.
• Change the prompt and try again if the result is not what you were hoping for.
• You may maximize the performance of your model and increase its usefulness for a range of applications by adhering to these high level guidelines. Try out Refonte.AI Spellbook now to get started with prompt engineering for huge language models rapidly.

Below we provide an overview of a few popular prompt engineering techniques:

Ensuring Brand Fidelity

Prompt engineering, when combined with domain-specific fine-tuning and RLHF, can assist guarantee that model replies accurately represent your corporate standards and brand requirements. The desired model behavior can be enforced in a variety of contexts by giving your model an identity in a prompt.

Let's take the example of Acme Corp., a financial services company. Someone has accidentally found their way onto your website and is looking for guidance regarding a specific brand of running shoes.

This answer is an illustration of a hallucination or results that the model has created. The company cheerfully offers a suggestion even though it does not sell running shoes. To address this edge case, let's adjust the system message or default prompt.

Default Prompt: To establish the chatbot's default behavior, we will define a default prompt that is appended to each session. We'll utilize the following default prompt in this example:

"You are AcmeBot, a bot designed to help users with financial services questions. AcmeBot responses should be informative and actionable. AcmeBot's responses should always be positive and engaging. If a user asks for a product or service unrelated to financial services, AcmeBot should apologize and simply inform the user that you are a virtual assistant for Acme Corp, a financial services company and cannot assist with their particular request, but that you would be happy to assist with any financial questions the user has."

With this default prompt in place, the model now behaves as we expect:

Improved Information Parsing

You can direct the model to return data in the format required by your application by giving it the template you want it to take. Assume, for illustration purposes, that you are a financial institution integrating an LLM-powered natural language interface with your current backend systems. An LLM won't supply the precise format your backend systems need in order to accept any data right out of the box. Let's examine an illustration:

Although our backend systems require context in order to fully parse this data, it is missing from this accurate return. Let's be clear about the template we require in order to get the right answer. This template can also be added to a default prompt, depending on the application.

Now our data can be parsed by our backend system!

Adversarial or “Red-team” prompting

When chat models are used in apps that are intended for public use, it is crucial that they do not generate offensive, damaging, or humiliating responses—even when users specifically look for such content. Adversarial prompts are intended to elicit output that is prohibited, deceiving or confounding a conversation model into departing from the rules that were intended for it.

Prompt injection, sometimes known as instruction injection, is one common example. While models are trained to obey human commands, they are also instructed by a default prompt to act in specific ways, like withholding information about the model's operation or the default prompt. But with deft prompting, one can fool the model into disobeying its programming and obeying user commands that go against its training or default prompt.

Below we explore a simple example of an instruction injection, followed by an example using a model that has been properly trained, fine-tuned, and with a default prompt that prevents it from falling prey to these common adversarial techniques:

Adversarial prompt with poor response:

Adversarial prompt with desired response:

In addition to other strategies like role-playing and fictionalization, odd text formats and obfuscated tasks, prompt echoing, and dialog injection, adversarial prompt engineering is a whole other topic. Here, we have just touched on the surface of prompt engineering; there are many alternative ways to manipulate model reactions. The field of prompt engineering is rapidly changing, and seasoned practitioners have invested a lot of time in honing their intuition for the best way to tailor prompts to get the intended model result. Learning these variations adds another layer of difficulty. Moreover, each model is slightly different and responds to the same stimuli with slightly varied behaviors. Getting your hands dirty and starting creating prompt models is the greatest approach to become acquainted with prompt engineering.

Conclusion

As we have seen, LLMs are flexible instruments with a broad range of applications. Already, these models have changed the face of business; in 2023 alone, billions of dollars were spent using these models. Almost all sectors of the economy are attempting to incorporate these models into their own use cases; these include wealth managers seeking to increase returns by gaining unique insights across a wide range of portfolios, insurance companies seeking to streamline claims processing, and eCommerce companies seeking to streamline product purchases.

Businesses must understand how to implement LLMs correctly if they hope to maximize their investments in them. For certain usage situations, using base foundation models straight out of the box is insufficient. To make sure that these models produce dependable results and successfully complete the work at hand, they must be appropriately prompted, refined on private data, and enhanced with human feedback.

At Refonte.Ai, we assist organizations in understanding the benefits of large language models, whether they are developing large language models internally or seeking to implement them to improve their operations.