Summary: Large language models (LLMs) are powerful artificial intelligence models that use deep learning and extensive datasets to generate text, perform translations, and create various types of content. There are two types of LLMs: proprietary and open source. While proprietary LLMs are owned by companies and require a license to use, open source LLMs are freely available to the public for modification and distribution. This article explores the benefits of open source LLMs, the types of projects they enable, and the risks associated with these models.
Benefits of Open Source LLMs
Open source LLMs offer several advantages:
Transparency and Flexibility
Enterprises can utilize open source LLMs within their infrastructure, giving them control over their data and reducing the risk of leaking sensitive information. Open source LLMs provide transparency in terms of their architecture, training data, algorithms, and usage, allowing for better trust, audits, and compliance. Furthermore, optimizing open source LLMs can improve performance and reduce latency.
Open source LLMs are generally more cost-effective than proprietary LLMs because they do not require licensing fees. However, operating an LLM still involves infrastructure costs.
Added Features and Community Contributions
Enterprise can customize open source LLMs by adding features and training them on specific datasets, without relying on a single vendor. Additionally, the open source nature of these models allows for contributions from a diverse community, enabling enterprises to stay at the forefront of technology and have more control over their technology choices.
Projects Enabled by Open Source LLM Models
Open source LLMs can be used for various projects, including:
Developing applications that can generate high-quality text for tasks like writing emails, blog posts, or creative stories.
Assisting developers in building applications, finding errors, and enhancing security by training LLMs on existing code and programming languages.
Creating personalized learning applications that cater to different learning styles.
Utilizing an LLM tool to extract essential data from long articles, news stories, or research reports.
Developing chatbots capable of understanding and responding to natural language conversations, answering questions, and offering suggestions.
Using LLMs trained on multilingual datasets for accurate and fluent translations in multiple languages.
Employing LLMs to analyze text and determine emotional or sentiment tones, which can be useful for brand reputation management and customer feedback analysis.
Content Filtering and Moderation
Identifying and filtering out inappropriate or harmful online content to maintain a safer online environment.
Organizations Utilizing Open Source LLMs
Various organizations across different sectors utilize open source LLMs:
- IBM and NASA developed an open source LLM for fighting climate change using geospatial data.
- Publishers and journalists use open source LLMs for internal analysis and information summarization.
- Healthcare organizations utilize open source LLMs for healthcare software, diagnosis tools, and patient information management.
- The financial industry uses open source LLMs like FinGPT specifically tailored for financial applications.
Noteworthy Open Source LLMs
Some of the notable open source LLMs include:
- LLaMa 2 by Meta AI, which offers pre-trained and fine-tuned generative text models available in the Watsonx.ai studio and the Hugging Face ecosystem.
- Bloom by BigScience, the first multilingual LLM trained with complete transparency.
- Falcon LLM from Technology Innovation Institute (TII), capable of generating creative text, solving complex problems, and automating repetitive tasks.
- MPT-7B and MPT-30B by MosaicML, licensed for commercial use and trained on 1T tokens.
- FLAN-T5 by Google AI, capable of handling over 1,800 diverse tasks.
- StarCoder by Hugging Face, an LLM coding assistant trained on permissive code from GitHub.
- RedPajama-INCITE, a pre-trained language model developed collaboratively by Together and various institutions.
- Cerebras-GPT by Cerebras, a family of GPT models ranging from 111 million to 13 billion parameters.
- StableLM by Stability AI, trained on a large dataset called “The Pile,” designed for image generation.
Risks Associated with Large Language Models
While LLMs offer many benefits, some risks need to be considered:
- Hallucinations can occur when LLMs generate false or misleading information based on incomplete or inaccurate data.
- Bias may arise if the training data is not diverse or representative.
- Consent refers to the compliance of the training data with AI governance processes and accountability.
- Security concerns include potential leaks of Personally Identifiable Information (PII), malicious use of LLMs by cybercriminals, and unauthorized changes to the model’s programming.
It is crucial to educate users about these risks and implement proper data and AI governance processes to mitigate them.
Open Source Large Language Models and IBM
IBM offers the watsonx platform, an enterprise-ready AI and data platform designed to help organizations leverage AI effectively. Through watsonx, organizations can train, deploy, and govern AI models, scale AI workloads, and enable transparent and explainable data and AI workflows. IBM’s watsonx Assistant, powered by open source LLMs, enhances customer understanding and facilitates conversational search and personalized assistance for developers and business users.
Frequently Asked Questions (FAQ)
1. What are large language models (LLMs)?
Large language models (LLMs) are AI models that utilize deep learning and extensive datasets to generate text, perform translations, and create various types of content.
2. What is the difference between proprietary and open source LLMs?
Proprietary LLMs are owned by companies and require a license for use, while open source LLMs are freely available to the public for modification and distribution.
3. What are the benefits of open source LLMs?
Open source LLMs offer transparency, flexibility, cost savings, added features, and community contributions. They provide transparency in terms of code, algorithms, and training data, allowing for increased trust and compliance. Open source LLMs also allow for customization and benefit from community contributions.
4. What types of projects can open source LLM models enable?
Open source LLM models can enable projects such as text generation, code generation, virtual tutoring, content summarization, AI-driven chatbots, language translation, sentiment analysis, and content filtering and moderation.
5. What are the risks associated with large language models?
Risks associated with large language models include hallucinations (generation of false information), bias (if the training data is not diverse or representative), consent (compliance of training data with AI governance processes), and security issues (such as PII leaks and unauthorized use of the model by cybercriminals).
6. How does IBM utilize open source large language models?
IBM’s watsonx platform incorporates open source large language models to enhance customer understanding, facilitate conversational search, and provide personalized assistance for developers and business users.