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Large Language Models (LLMs) have revolutionized natural language processing and artificial intelligence. They are trained on massive amounts of text data and can generate text, answer questions, and perform other tasks. When it comes to implementing LLMs in internal AI projects, a crucial decision to make is whether to use open or closed source language models. In this article, we will present the advantages and disadvantages of both options.

 

Open Source LLMs

Open Source LLMs offer a host of advantages that make them a compelling choice for organizations looking to leverage language models for their AI projects. Here, we’ll delve deeper into the benefits and considerations of using open source LLMs.

Benefits of Open Source LLMs:

  • Control and Flexibility: Open source LLMs provide organizations with an unparalleled level of control. You have the freedom to modify the model, tailor it to your specific needs, and adapt it to various applications. This level of flexibility allows you to fine-tune the model to perform tasks that are highly relevant to your business.
  • Customization: The open nature of these models means that their underlying architecture and weight parameters are accessible. This accessibility makes customization relatively straightforward. Developers can tweak the model to improve performance or adapt it to domain-specific requirements, giving your organization a competitive edge.
  • Community Support: Open source LLMs often benefit from the collective wisdom and expertise of a large and diverse developer community. This community can provide valuable insights, enhancements, and updates to the model. As a result, your organization can tap into the knowledge of a global network of experts who contribute to the ongoing development of these models.
  • Innovation: The open-source ecosystem is known for its culture of innovation. It thrives on collaboration and rapid adaptation to emerging technologies and trends. When you opt for an open source LLM, you can harness the cutting-edge advancements that arise from this dynamic environment, enabling your organization to stay ahead of the curve.
  • Transparency: Open source LLMs provide transparency into the inner workings of the model. This transparency is not only valuable from a technical standpoint but also from an ethical one. It allows organizations to build trust with their customers by demonstrating that the AI systems they deploy are accountable and explainable.

Disadvantages of Open Source LLMs:

  • Limited Resources: While the open-source community is vibrant and collaborative, it may not always have the same level of resources as large corporations developing closed-source models. This can lead to limitations in terms of research, development, and support for open source LLMs.
  • Dependency on the Community: The development and improvement of open source LLMs heavily rely on community contributions. While the community’s dedication is often commendable, it may not always provide consistent and reliable updates or support, which could be a challenge for organizations with specific deadlines or stringent requirements.

 

Open Source LLMs

  1. LLaMA 2Meta’s commitment to openness in the LLM space is evident with the release of its powerful open-source Large Language Model, Meta AI (LLaMA), and its upgraded version, LLaMA 2. Launched in July 2023 for both research and commercial use, LLaMA 2 is a pre-trained generative text model with 7 to 70 billion parameters. Fine-tuned using Reinforcement Learning from Human Feedback (RLHF), it serves as a versatile generative text model applicable to chatbots and various natural language generation tasks. Meta has introduced two open, customized versions of LLaMA 2, namely Llama Chat and Code Llama.
  2. BLOOM Introduced in 2022 through a collaborative project involving volunteers from 70+ countries and researchers from Hugging Face, BLOOM is a powerful autoregressive Open Source LLM. With 176 billion parameters, it excels in generating coherent and accurate text across 46 languages and 13 programming languages. Transparency is a key aspect of BLOOM, as its source code and training data are accessible to everyone through the Hugging Face ecosystem.
  3. BERT (Bidirectional Encoder Representations from Transformers), developed by Google in 2018, stands out as an influential open-source LLM. Leveraging transformer neural architecture, BERT quickly achieved state-of-the-art performance in numerous natural language processing tasks. Its open-source nature has contributed to its popularity, with thousands of pre-trained models available for specific applications such as sentiment analysis, clinical note analysis, and toxic comment detection.
  4. Falcon 180B Released by the Technology Innovation Institute of the United Arab Emirates in September 2023, Falcon 180B demonstrates the closing gap between proprietary and open-source LLMs. With 180 billion parameters and 3.5 trillion tokens, Falcon 180B outperforms competitors like LLaMA 2 and GPT-3.5 in various NLP tasks. Although free for commercial and research use, it demands substantial computing resources.
  5. OPT-175B Meta’s Open Pre-trained Transformers Language Models (OPT) marked a milestone in liberating the LLM landscape through open source. OPT-175B, one of the most advanced open-source LLMs, rivals GPT-3 in performance. While available for research purposes, it is released under a non-commercial license, restricting its use for AI-driven companies.
  6. XGen-7B Salesforce’s entry into the LLM race, XGen-7B, emphasizes supporting longer context windows, allowing for an 8K context window in its most advanced variant. Despite using only 7B parameters for training, XGen prioritizes efficiency. Available for commercial and research purposes, some variants are released under a non-commercial license.
  7. GPT-NeoX and GPT-J Developed by researchers from EleutherAI, GPT-NeoX and GPT-J serve as open-source alternatives to GPT. With 20 billion and 6 billion parameters, respectively, they deliver accurate results across various natural language processing tasks. Trained with diverse high-quality datasets, these LLMs cover multiple domains and are accessible for free through the NLP Cloud API.
  8. Vicuna 13-Bb is an open-source conversational model derived from fine-tuning the LLaMa 13B model using user-shared conversations from ShareGPT. With applications across industries like customer service, healthcare, education, finance, and travel/hospitality, Vicuna-13B has demonstrated impressive performance, outperforming other models in over 90% of cases according to a preliminary evaluation using GPT-4 as a judge.

In summary, open source LLMs provide a unique set of advantages, including control, customization, community support, innovation, and transparency. These benefits empower organizations to harness the full potential of language models while aligning them with their specific needs. However, it’s essential to consider the potential drawbacks, such as limited resources and a degree of dependency on the open-source community when making a decision regarding their implementation in AI projects.

Closed Source LLMs, also known as proprietary language models, present a contrasting approach to their open source counterparts. These models have their source code kept confidential and are typically developed and maintained by large corporations. In this section, we will explore the distinctive benefits and considerations associated with the use of closed source LLMs in AI projects.

Benefits of Closed Source LLMs:

  • Abundant Resources: One of the primary advantages of closed source LLMs is the substantial financial backing and resources provided by the corporations behind them. These resources can be channeled into extensive research, development, and continuous improvement of the model. As a result, organizations can rely on a robust, well-supported solution for their AI projects.
  • Dedicated Support: Closed source LLMs often come with dedicated support from the corporation that developed them. This means that organizations using these models can access professional assistance, troubleshooting, and expert guidance. This level of support can be invaluable in ensuring the successful deployment and performance of AI systems.

Disadvantages of Closed Source LLMs:

  • Limited Control: Perhaps the most significant drawback of closed source LLMs is the limited level of control they offer to organizations. With the source code being proprietary and inaccessible, customization and fine-tuning become challenging. This limitation can hinder the adaptability of the model to specific business needs.
  • Limited Customization: The inability to access and modify the underlying architecture and weight parameters of closed source LLMs means that organizations are restricted in their ability to tailor the model to suit their unique requirements. This can lead to suboptimal performance in certain applications.

Lack of Transparency: Closed source LLMs often lack transparency into their internal processes. Organizations using these models may not have full visibility into how the model arrives at its predictions or decisions. This lack of transparency can be a significant concern, especially in applications where accountability and explain ability are critical.

Examples of closed LLM models

  1. HyperCLOVA: Naver Corp’s HyperCLOVA, an AI model designed for the Korean language, was introduced in May 2021. The company is gearing up to launch an upgraded version, HyperCLOVA X, in July, capable of comprehending both images and speech in a multimodal format. Termed the Korean GPT-3, it has been trained on an extensive corpus of 560 billion tokens. According to Kim Yu-won, CEO of Naver Cloud Corp, this model has the potential to revolutionize natural language processing.
  2. Gopher: DeepMind’s Gopher is a transformer language model with an impressive 280 billion parameters. Researchers assert that this model significantly narrows the accuracy gap between GPT-3 and human expert performance, surpassing forecasted expectations and outperforming current state-of-the-art language models in approximately 81% of tasks.
  3. Chinchilla: Adding to DeepMind’s series of animal-inspired models, Chinchilla is a 70 billion parameter model designed for optimal computational efficiency. Trained on a dataset containing 1.4 trillion tokens, Chinchilla was found to be optimally trained by maintaining a balance between model size and training tokens. Despite utilizing the same computational budget as Gopher, Chinchilla boasts four times more training data, positioning itself as a robust contender in the language model landscape.
  4. BloombergGPT: Recently, Bloomberg introduced BloombergGPT, a new, large-scale generative AI model specifically tailored to address the intricate landscape of the financial industry. This highly trained language model is optimized for parsing and processing vast amounts of financial data, showcasing promise in the field of Natural Language Processing (NLP).

In conclusion, the decision to employ closed source LLMs in AI projects should be guided by an organization’s specific needs and priorities. The advantages of abundant resources and dedicated support are offset by the limitations of limited control, customization, and transparency. Therefore, it is essential for organizations to carefully evaluate their requirements and objectives before choosing between open source and closed source LLMs to ensure that they align with their long-term AI strategy.

The choice between open source and closed source LLMs in artificial intelligence projects depends on the specific needs and priorities of an organization. Here are examples of industries where each of these options may be preferred:

Best Use of Open Source LLMs:

  • Scientific Research: In fields related to scientific research, such as biology, medicine, or physics, open source LLMs allow for the customization of models for specific experiments and analyses.
  • Education: In the education sector, where there is a need to adapt models to different fields and pedagogical applications, open source models are valuable tools.
  • Startups: Small businesses and startups often use open source models due to their accessibility, flexibility, and lower initial costs.
  • Open Collaboration Projects: In open source projects, such as the development of free software or content, open source models are consistently utilized.
  • Customized Business Applications: In industries where there is a need to tailor models for specific business applications, open source models can be a favorable solution.

Best Use of Closed Source LLMs:

  • Financial Industry: In the financial sector, where there are rigorous regulations and security requirements, closed source models can provide dedicated support and confidence in results.
  • Medicine and Healthcare: In fields related to medicine, where reliability and accuracy are critical, closed source models offer the resources and support needed to meet the highest standards.
  • Industrial Control Systems: In the case of industrial control systems, where reliability and stability are essential, closed source models ensure certainty in their operation.
  • Security and Data Protection: In areas related to security and data protection, where confidentiality and control are of utmost importance, closed source models offer greater assurance.
  • Integrated Solution Providers: Companies specializing in providing integrated solutions often prefer closed source models due to their compatibility with other tools and software.

The final decision depends on an organization’s individual requirements, budget, regulations, and priorities. In many cases, considering the advantages and disadvantages of both options is crucial in the decision-making process.

 

Author

  • Krzysztof Kacprzak

    Krzysztof is a seasoned Data Engineering expert with a focus on the broader aspects of data architecture and management.For the past five years, he has played a pivotal role in the DS Stream company, serving as its Chief Technology Officer (CTO).Beyond his technological pursuits, Krzysztof holds an LLM degree, showcasing his multifaceted expertise.His vast experience encompasses not only the tech world but also spans sectors like Retail, Banking and Telecommunications.Apart from his hands-on roles, Krzysztof is instrumental in complex project cost evaluations, sales activities, and strategic requirement analyses.

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