By Ramakrishnan Jonnagadla, Vice President-Engineering, Ascendion
In the realm of artificial intelligence, generative AI has emerged as a transformative force, revolutionising every aspect of our lives, and asking every individual to take a pause and relook at how things are being done. At the heart of this revolution lie large language models (LLMs), which harness the power of deep learning and massive datasets to generate human-quality text. With over 325,000 models available on Hugging Face and countless more in development, the question arises: why should one consider using open source LLMs?
Open source or Proprietary LLMs:
Generative AI models can be broadly categorised into two types: proprietary and open source.
Proprietary (or closed source) LLMs
Are owned by a company who can control its usage
May include a license that restricts how the LLM can be used.
Examples: OpenAI’s GPT, Google’s Bard, Anthropic’s Claude 2 Open source LLMs
Are free and available for anyone to access, fostering collaboration & innovation
Developers and researchers are free to use, improve or otherwise modify the model
Examples: Hugging Face, Meta’s LLaMA 2, Databricks Dolly, TII’s Falcon
It’s not true in every instance, but generally many proprietary LLMs are far larger in size than open-source models, specifically in terms of parameter size. Some of the leading proprietary LLMs extend to
thousands of billions of parameters. But “bigger isn’t necessarily better”. However, the open-source model ecosystem shows promise in challenging the proprietary LLM business models in many use cases
Some pertinent questions on open-source LLMs to look at are:
a. Why should one consider open-source LLMs?
b. Who are the early adopters of open-source LLMs?
c. What are some of the leading open source models available today?
d. What are the common risks associated with using them?
Why should one consider open-source LLMs?
Transparency: Open-source LLMs provide insights into their inner workings, allowing users to
understand how they generate text. This transparency fosters trust and enables developers to identify
and address potential biases.
Fine-tuning: Open source LLMs can be fine-tuned to specific tasks and domains, tailoring them to unique
use cases. This adaptability makes them versatile tools for diverse applications.
Community collaboration: Open-source LLMs benefit from the collective knowledge and expertise of a
global community of developers and researchers. This collaborative environment drives innovation and
accelerates model development.
Who are the early adopters of Open Source LLMs?
Healthcare: Open-source LLMs are being used to develop diagnostic tools and optimise treatment plans,
improving healthcare outcomes.
Finance: FinGPT, an Open source LLM specifically tailored for the financial industry, is assisting with
financial modeling and risk assessment.
Aerospace: NASA has developed an Open source LLM trained on geospatial data, aiding in satellite
imagery analysis and mission planning.
And many more industries are rapidly adopting LLMs to solve unique business problems.
What are some of the leading open-source models available today?
Companies like Hugging Face maintain an open LLM leaderboard, that tracks, ranks, and evaluates open source LLMs on various benchmarks like which LLM is scoring highest on the “Truthful AI Benchmark series”, which measures whether a language model is truthful in generating answers to questions. The top spots on these leaderboards, change frequently. And it’s quite fun to watch the progress these models are making.
Some examples of top models include:
— Llama 2 Developed by Meta AI, Llama 2 encompasses a range of generative text models, from 70
billion to 7 billion parameters, offering flexibility for different applications.
— Vicuna Built upon the Llama model, it is specifically fine-tuned to follow instructions, making it ideal
for task-oriented applications.
— Bloom Created by BigScience, it is a multilingual LLM developed collaboratively by over 1000 AI researchers, demonstrating the power of community-driven innovation.
What are the common risks associated with using them?
Although LLM outputs often sound fluent and authoritative, they can be confidently wrong.
Hallucinations: LLMs can generate false or misleading information, especially when trained on
incomplete or inaccurate data.
Bias: LLMs can reflect biases present in their training data, leading to discriminatory or unfair outcomes.
Security Concerns: LLMs can be misused for malicious purposes, such as leaking sensitive information or
generating phishing scams.
As Open-source LLMs continue to mature and gain traction, it is evident that they are poised to play a
transformative role in the future of AI. Their potential to democratise AI access, foster innovation, and
solve real-world problems is immense. However, it is crucial to acknowledge and mitigate the associated
risks to ensure responsible and ethical development and deployment of these powerful tools.