State of the LLM: Unlocking Business Potential with Large Language Models
Machine Learning Engineer
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Over the past few months, there has been a surge of interest in large language models (LLMs). These models, which use artificial intelligence to generate human-like language, have captured public interest like never before, thanks mainly to the release of OpenAI’s ChatGPT. This explosion of public interest has meant that other companies and groups have followed suit, either by releasing base models like Meta AI’s LLaMA or instruction tuning previously released base models like Databricks’ Dolly or BAIR’s Koala.
Now is the perfect time for businesses to explore the potential of LLMs and integrate them into their strategy. In this blog post, we will guide you through the current state of the LLM landscape in 4 parts: Models, Tooling, Challenges and Applications. We’ll also provide some tips and key takeaways that you can use to decide on how to use LLMs in your business strategy. Let’s dive in!
Part 1: Models
Most LLMs that are centerstage today are similar in terms of their architecture. However, what mainly distinguishes models are their size (number of parameters) and the quality and quantity of training data used. While GPT-3 has 175 billion parameters, models such as LLaMA are much smaller, with versions ranging from 7 billion to 65 billion parameters.
Tip: It has been found that data quality is much more important than data quantity. If you are exploring an LLM use case for your business, optimizing for data quality will pay better dividendsthan increasing the amount of data.
However, the difference between base models like GPT-3/LLaMA and their instruction following variants like ChatGPT/Vicuna is an additional training step. This step can use human feedback (called Reinforcement Learning with Human Feedback (RLHF)) or a supervised dataset of instructions and their corresponding responses.
Based on current wisdom, RLHF is the better option to train an LLM to follow instructions and respond in a human-like manner. However, why this is so has yet to be fully understood. We recommend John Schulman’s talk for a better understanding on the advantages of RLHF.
Currently, the best instruction-following model that’s available for public use is ChatGPT. At 175 billion parameters and trained with RLHF, it towers above the currently available open-source models. Most open-source instruction-following models like Alpaca, Vicuna, Dolly, and Koala are much smaller and tuned using a supervised dataset containing data generated by a technique called Self-Instruct or by ChatGPT. Efforts like Open Assistant should help create comparable open-source alternatives soon.
Key Takeaway: While ChatGPT currently leads the market in terms of performance, open-source models are steadily improving and are viable options for specific use cases. Keeping an eye on the development of both proprietary and open-source LLMs will ensure that your business can make the best choice.
Part 2: Tooling
New techniques and models have always led to the creation of new tools to improve their ease of use. LLMs are no different! In this section, we’ll explore two emerging tools that have been developed to address the unique challenges posed by LLMs: PEFT (Parameter Efficient Finetuning) — a collection of techniques that helps finetune large models without breaking the bank, and Langchain — a framework for developing applications powered by language models.
PEFT: Parameter Efficient Finetuning
One of the critical challenges in the LLM landscape is finetuning these models without incurring prohibitive costs. Since these models have billions of parameters, adapting them to custom tasks or datasets can be expensive.
Over the last few years, efficient finetuning techniques like LoRA, Prefix tuning, and Prompt tuning have made LLMs more accessible for practitioners with limited resources with comparable or better performance at a small fraction of the cost. You can find a technical deep dive on the popular efficient finetuning technique LoRA, here.
Tip: Hugging Face’s recently released PEFT library contains all these techniques and more. With its native integration with the Transformers and Accelerate libraries along with the ability to use other optimization libraries like bitsandbytes, developers can easily finetune large language models on specific tasks using less computing power and, thus, affordably.
Langchain: Building Composable LLM Applications
The more data an LLM can access, the more useful it will be. However, creating an LLM application with multiple components communicating with various data sources can take time and effort, and this is where Langchain finds its niche.
Langchain is a framework for building composable LLM applications. Available in both Python and TypeScript, the Langchain library offers a plethora of tooling and third-party integrations (major cloud providers, Google Drive, Notion and more!) to build powerful applications (‘chains’) driven by LLMs. It also provides pre-built chains capable of some of the most common LLM applications like Retrieval Augmented Generation (RAG) or chat.
From our experience, Langchain offers some nifty components that can be used to implement quick proof of concepts and simple applications to incorporate LLMs into your business or daily life. However, one might find it limiting while working on custom use cases, especially in production where greater control and flexibility are necessary.
Key Takeaway: The emergence of powerful techniques and tools like LoRA, PEFT and Langchain represents a game-changing opportunity for businesses to leverage the capabilities of LLMs fully. Finetuning an LLM on a specific task/dataset is now more accessible. Tools like Langchain are improving the ease with which these models can interact with multiple external sources of information.
Part 3: Challenges
While the potential is immense, it is essential to recognize that harnessing the full capabilities of LLMs comes with challenges. This section will focus on two primary hurdles: LLMOps and Model Bias. By understanding these challenges and how to overcome them, businesses can effectively capitalize on the opportunities offered by LLMs and stay ahead of the competition.
LLMOps: Scaling and Managing LLM Infrastructure
As LLMs grow in size and complexity, the infrastructure required to train, finetune, and deploy them becomes increasingly demanding, introducing the challenge of LLMOps — a specialized subdomain of MLOps that focuses on the unique operational aspects of working with large language models.
Some of the critical challenges in LLMOps include the following:
Working with the ambiguities and intricacies of natural language
Optimizing the costs and latency of operating LLMs in production
Establishing a robust model re-training and monitoring setup
For a deep dive into the specific challenges of using LLMs in production, we highly recommend this blog by Chip Huyen. While new tools will emerge to solve these specific challenges, currently, businesses might have to build bespoke solutions for their particular use cases. In these situations, leveraging the experience of ML experts might prove invaluable.
Model Bias and Ethics: Ensuring Fair and Responsible AI
Another critical challenge in the world of LLMs is addressing model bias and ensuring ethical AI practices. Since LLMs are trained on vast amounts of data from the internet, they can inadvertently learn and propagate biases present in the data. This can lead to unintended consequences when deploying LLMs in real-world applications.
To address these concerns, one should:
Implement guidelines for responsible AI usage that consider both the intended and unintended consequences of deploying LLMs.
Use tools and techniques to monitor and mitigate biases in the models during the training and finetuning processes.
Collaborate with diverse teams and stakeholders to ensure a well-rounded perspective on potential ethical issues.
To the extent possible, establish safeguards against unintended use of LLMs by the end user.
By taking these steps and informing their end users about the limitations of LLMs in the context of their application, organizations can leverage the models responsibly and build trust with customers, regulators, and the broader public.
Key Takeaway: The two significant challenges businesses must overcome to harness the full capabilities of LLMs are LLMOps and Model Bias.While there will be general solutions to the challenge of LLMOps in the future, for now, bespoke solutions are the way to go. MLOps experts are invaluable here. Solving model bias is quite tricky. However, testing, safeguards and transparency can ensure the best outcomes.
Part 4: Applications
The potential applications of LLMs are vast and rapidly growing. We’re in the early stages of this technology transforming industries as a whole, and businesses that take the first step might find themselves way ahead of the competition in the future.
In this section, we will explore some of the most promising applications of LLMs, including Retrieval Augmented Generation (RAG), NLP tasks like summarization and extracting structured data, and customer support. Finally, we’ll take a brief look at process automation using AutoGPT.
Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation is a technique that combines the strengths of LLMs with the power of information retrieval systems. By using RAG, LLMs can access external knowledge sources to generate current, informed and accurate responses, making them even more valuable in a wide range of applications. For example, a RAG system implemented on internal documentation and resources would prove invaluable as it would tremendously boost productivity by putting the entire company’s resources at their employee’s fingertips.
You can find our deep dive on Retrieval Augmented Generation systems here!
NLP Tasks: Summarization and Extracting Structured Data
LLMs excel in various natural language processing tasks, enabling businesses to streamline their workflows and make better use of unstructured data. For instance, LLMs can be used for:
Summarization: Automatically generating concise summaries of lengthy documents, such as reports or articles, saving time and effort for employees and enhancing decision-making processes. For example, a financial firm could use LLMs to generate executive summaries of lengthy market research reports, providing analysts with easily digestible insights and facilitating more informed decisions.
Extracting Structured Data: Transforming unstructured data, like customer feedback or news articles, into structured formats that can be easily analyzed and used for further processing, such as sentiment analysis or trend detection. For example, a retail company could leverage LLMs to extract key information from customer reviews, enabling the business to identify product trends and common issues and ultimately improve the customer experience.
The most obvious use of an application like ChatGPT, LLMs can revolutionize customer support by providing fast, accurate, and personalized responses to customer inquiries. By integrating LLMs into chatbots or helpdesk systems, businesses can significantly improve customer satisfaction and reduce response times, freeing human agents to focus on more complex issues requiring their expertise.
Workflow Automation with AutoGPT
The previously mentioned use cases are some of the more tried and tested applications that we see being used across industries and domains. Workflow automation with AutoGPT is relatively new. One can find anecdotes of this application at young, small companies all over Twitter and LinkedIn, but rigorous testing and stakeholder involvement is necessary before using it at a large scale in your business.
AutoGPT is an experimental technique that uses LLMs to create autonomous agents which fulfil various tasks based on natural language inputs. While still in the early stages of development, AutoGPT shows great promise! One can automate operations, sales and HR workflows using such autonomous agents to state some examples. For example, one can set up AutoGPT to generate new sales leads, which includes finding the email-id of a contact at a prospective client, adding them to the CRM software and then sending them an email.
You can find an in depth dive into the world of AutoGPT in our blog here!
Key Takeaway: Applications such as RAG, summarization, and customer support are the bread and butter of LLMs. They have repeatedly been proven to be adept at such applications, even when they were less powerful than they are right now. With the new generation of large language models, these applications and more will be accurate, autonomous and even more useful for organizations and their customers alike. Businesses are only limited by the imagination of their decision-makers.
Throughout this blog, we have explored the current state of the LLM landscape, delving into the architecture, techniques, challenges, and applications of these powerful models. With the rapid advancements in LLM technology and the development of new tools and solutions, it has become increasingly clear that there has never been a better time for businesses to invest in LLMs.
LLMs offer an unprecedented opportunity for businesses to innovate, streamline processes, and improve customer experiences, providing a competitive edge that will set them apart in their respective industries. From Retrieval Augmented Generation to summarization, customer support, and even experimental applications like AutoGPT, LLMs are reshaping the landscape of business operations and applications.
However, this opportunity also comes with challenges like LLMOps and model bias. By partnering with experts in the field, businesses can navigate these complexities and responsibly harness the full potential of LLMs, driving innovation and creating value for their organizations.
These are early days in the new LLM world, but many businesses are already taking the first step and integrating this technology into their workflow. By investing in LLM expertise and adopting this groundbreaking technology, businesses can unlock unparalleled potential and stay ahead in the ever-evolving competitive landscape.