The Challenges Associated With Building Products Using Large Language Models (LLMs)
Hello there.
I hope you all are doing well. It has been quite some time since my last publication on Medium. Recently, I’ve dedicated some time to thoroughly explore the newly found love of every serial startup founders out there, popularly known as Large Language Models, or LLMs.
Now, I don’t want to talk about what are LLMs, how they work, how to build your own LLMs using HuggingFace and LangChain etc. You will find plenty of them with a simple google search.
Rather, I want to put some light into a lesser discussed topic regarding the LLMs : The challanges of putting LLMs into work.
In recent years, large language models have emerged as a groundbreaking technology with significant potential across various industries. These models, such as GPT-3.5, have demonstrated remarkable abilities in natural language processing, opening up new avenues for innovation.
However, while these models offer immense opportunities, they also present economic and operational challenges that businesses must navigate to harness their full potential. In this article, we will explore the key hurdles faced when building products using large language models and discuss strategies to overcome them.
Now, you may ask, why we need to build products using LLMs when I can simply subscribe to OpenAI chatGPT. The simple answer is Data privacy and IP protection.
Are you comfortable sharing your personal data to Google, Microsoft, Meta etc? I am sure you are not. Infact, from time to time, we have seen so many data privacy related ‘renaissance’. But reccently, in last 6 months, these magical LLMs (chatGPT, BARD etc) controlled by few giants have gained so much popularity and users have almost forgot about data privacy and IP protection. People are passing confidential information to these ‘magical models’ without understanding how they work.
My point is, what happened in last 6 months, will not continue to happen in the long run. There will be a ‘renaissance’ again and then we have to build purpose driven ‘private chatGPT’. Let’s deep dive.
Summary
I. Availability of Infrastructure :
Yes. A100. That’s what every founder is looking everywhere. A100 GPU card that has 80 GB of GPU memory is the best NVIDIA is offering right now. AWS, Azure, GCP all of them has NVIDIA A100 GPU card powered machines. But the catch is it’s not readily available for everyone. There will be a long thread of emails and long waiting time with customer support explaining the reason, usage, business information, future plan to unlock a machine like that. When you get one, it will start costing you $8 USD per card per hour on average.
II. Ethical Considerations and Bias Mitigation :
Large language models have garnered attention due to their ability to generate human-like text. However, they can also inadvertently perpetuate biases present in the training data, leading to ethical concerns. Addressing bias and ensuring fairness in language models requires dedicated efforts, often involving additional time, resources, and expertise. One of the best practices to reduce bias is Reinforcement Learning from Human Feedback (RLHF). Companies must invest in rigorous data screening, algorithmic auditing, and ethical guidelines to mitigate bias. Failure to do so can have long-term consequences, including damage to brand reputation, legal repercussions, and loss of customer trust. Incorporating ethical considerations from the early stages of product development can help prevent such issues and minimize impact. To further reduce the impact, you can definately do effective prompt enginering.
III. Cost of Training :
So after reading the above section, you are convinced that we need to retrain the open source LLMs with purpose driven dataset. One of the primary economic challenges of utilizing large language models is the substantial cost associated with training. Training these models requires significant computational resources and it’s a time-consuming processes. The hardware requirements and energy consumption can strain budgets, particularly for smaller businesses. Moreover, as the demand for computational resources increases, the associated costs can skyrocket. To tackle this challenge, companies can explore strategies for optimizing training pipelines to maximize efficiency and minimize expenses. To give you an approximate idea, reatraining a 12B parameter model would cost around $300 USD. Higher the no. of parameters, higher the cost. To reduce the retraining cost, you can definately do effective grounding.
IV. Data Acquisition and Preparation for Retraining :
Large language models thrive on vast amounts of high-quality data. Acquiring and preparing such data can present financial and legal hurdles for businesses. Building comprehensive and diverse datasets demands extensive resources, both in terms of time and money. Additionally, the costs associated with data cleaning, preprocessing, and annotation can be significant. Startups and smaller companies often face difficulties in accessing and curating large-scale datasets, potentially limiting their ability to fully utilize language models. Collaborations, partnerships, and open-source initiatives can provide avenues for shared data resources, reducing costs and enabling broader access to high-quality datasets. By the way, if you have just lit an ‘60W vintage bulb’ in your head and go use chatGPT to create training data, blow the bulb immediately. They will sue you.
V. Expertise and Talent :
Developing products using large language models requires expertise in machine learning, natural language processing, and software development. Acquiring and retaining skilled professionals in these domains can be a considerable challenge. The demand for experts in the field often outpaces supply, leading to high salary expectations and intense competition for top talent. Building a capable team capable of maximizing the potential of language models can strain budgets, particularly for smaller organizations. One solution is to use open-source tools and libraries which can help bridge the expertise gap, enabling developers to leverage pre-existing solutions and reduce development costs.
VI. Deployment and Scaling:
Deploying large language models in real-world scenarios and scaling them to handle increased user demand can pose financial challenges. The computational requirements and infrastructure costs associated with serving predictions in real-time can be substantial. Companies need to strike a balance between performance and cost to ensure the efficient and cost-effective deployment of language models. Or simply, charge your customers more.
Leveraging large language models presents businesses with transformative potential but also economic and operational obstacles. To overcome these challenges, a comprehensive strategy is needed, encompassing strategic planning, resource optimization, collaboration, and ethical considerations. This entails addressing costs associated with training and infrastructure, acquiring and preparing data, acquiring expertise and talent, and considering ethical implications. By effectively managing deployment and scaling, businesses can fully tap into the benefits of large language models, leading to innovation across industries.
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The Challenges Associated With Building Products Using Large Language Models (LLMs) was originally published in Artificial Intelligence in Plain English on Medium, where people are continuing the conversation by highlighting and responding to this story.
https://ai.plainenglish.io/the-challenges-associated-with-building-products-using-large-language-models-llms-a0573dcfdf5f?source=rss—-78d064101951—4
By: Hazzlenut
Title: The Challenges Associated With Building Products Using Large Language Models (LLMs)
Sourced From: ai.plainenglish.io/the-challenges-associated-with-building-products-using-large-language-models-llms-a0573dcfdf5f?source=rss—-78d064101951—4
Published Date: Thu, 08 Jun 2023 02:00:01 GMT
