The Battle of Open Source vs Closed Source Language Models: A Technical Analysis

open source vs close source LLM

Large language models (LLMs) have captivated the AI community in recent years, spearheading breakthroughs in natural language processing. Behind the hype lies a complex debate – should these powerful models be open source or closed source?

In this post, we’ll analyze the technical differentiation between these approaches to understand the opportunities and limitations each presents. We’ll cover the following key aspects:

  • Defining open source vs closed source LLMs
  • Architectural transparency and customizability
  • Performance benchmarking
  • Computational requirements
  • Application versatility
  • Accessibility and licensing
  • Data privacy and confidentiality
  • Commercial backing and support

By the end, you’ll have an informed perspective on the technical trade-offs between open source and closed source LLMs to guide your own AI strategy. Let’s dive in!

Defining Open Source vs Closed Source LLMs

Open source LLMs have publicly accessible model architectures, source code, and weight parameters. This allows researchers to inspect internals, evaluate quality, reproduce results, and build custom variants. Leading examples include Anthropic’s ConstitutionalAI, Meta's LLaMA, and EleutherAI's GPT-NeoX.

In contrast, closed source LLMs treat model architecture and weights as proprietary assets. Commercial entities like Anthropic, DeepMind, and OpenAI develop them internally. Without accessible code or design details, reproducibility and customization face limitations.

Architectural Transparency and Customizability

Access to open source LLM internals unlocks customization opportunities simply not possible with closed source alternatives.

By adjusting model architecture, researchers can explore techniques like introducing sparse connectivity between layers or adding dedicated classification tokens to enhance performance on niche tasks. With access to weight parameters, developers can transfer learn existing representations or initialize variants with pre-trained building blocks like T5 and BERT embeddings.

This customizability allows open source LLMs to better serve specialized domains like biomedical research, code generation, and education. However, the expertise required can raise the barrier to delivering production-quality implementations.

Closed source LLMs offer limited customization as their technical details remain proprietary. However, their backers commit extensive resources to internal research and development. The resulting systems push the envelope on what’s possible with a generalized LLM architecture.

So while less flexible, closed source LLMs excel at broadly applicable natural language tasks. They also simplify integration by conforming to established interfaces like the OpenAPI standard.

Performance Benchmarking

Despite architectural transparency, measuring open source LLM performance introduces challenges. Their flexibility enables countless possible configurations and tuning strategies. It also allows models prefixed as “open source” to actually include proprietary techniques that distort comparisons.

Closed source LLMs boast more clearly defined performance targets as their backers benchmark and advertise specific metric thresholds. For example, Anthropic publicizes ConstitutionalAI’s accuracy on curated NLU problem sets. Microsoft highlights how GPT-4 surpasses human baselines on the SuperGLUE language understanding toolkit.

That said, these narrowly-defined benchmarks faced criticism for overstating performance on real-world tasks and underrepresenting failures. Truly unbiased LLM evaluation remains an open research question – for both open and closed source approaches.

Computational Requirements

Training large language models demands extensive computational resources. OpenAI spent millions training GPT-3 on cloud infrastructure, while Anthropic consumed upwards of $10 million worth of GPUs for ConstitutionalAI.

The bill for such models excludes most individuals and small teams from the open source community. In fact, EleutherAI had to remove the GPT-J model from public access due to exploding hosting costs.

Without deep pockets, open source LLM success stories leverage donated computing resources. LAION curated their tech-focused LAION-5B model using crowdsourced data. The non-profit Anthropic ConstitutionalAI project utilized volunteer computing.

The big tech backing of companies like Google, Meta, and Baidu provides closed source efforts the financial fuel needed to industrialize LLM development. This enables scaling to lengths unfathomable for grassroots initiatives – just see DeepMind’s 280 billion parameter Gopher model.

Application Versatility

The customizability of open source LLMs empowers tackling highly specialized use cases. Researchers can aggressively modify model internals to boost performance on niche tasks like protein structure prediction, code documentation generation, and mathematical proof verification.

That said, the ability to access and edit code does not guarantee an effective domain-specific solution without the right data. Comprehensive training datasets for narrow applications take significant effort to curate and keep updated.

Here closed source LLMs benefit from the resources to source training data from internal repositories and commercial partners. For example, DeepMind licenses databases like ChEMBL for chemistry and UniProt for proteins to expand application reach. Industrial-scale data access allows models like Gopher to achieve remarkable versatility despite architectural opacity.

Accessibility and Licensing

The permissive licensing of open source LLMs promotes free access and collaboration. Models like GPT-NeoX, LLaMA, and Jurassic-1 Jumbo use agreements like Creative Commons and Apache 2.0 to enable non-commercial research and fair commercialization.

In contrast, closed source LLMs carry restrictive licenses that limit model availability. Commercial entities tightly control access to safeguard potential revenue streams from prediction APIs and enterprise partnerships.

Understandably, organizations like Anthropic and Cohere charge for access to ConstitutionalAI and Cohere-512 interfaces. However, this risks pricing out important research domains, skewing development towards well-funded industries.

Open licensing poses challenges too, notably around attribution and liability. For research use cases though, the freedoms granted by open source accessibility offer clear advantages.

Data Privacy and Confidentiality

Training datasets for LLMs typically aggregate content from various online sources like web pages, scientific articles, and discussion forums. This risks surfacing personally identifiable or otherwise sensitive information in model outputs.

For open source LLMs, scrutinizing dataset composition provides the best guardrail against confidentiality issues. Evaluating data sources, filtering procedures, and documenting concerning examples found during testing can help identify vulnerabilities.

Unfortunately, closed source LLMs preclude such public auditing. Instead, consumers must rely on the rigor of internal review processes based on announced policies. For context, Azure Cognitive Services promises to filter personal data while Google specifies formal privacy reviews and data labeling.

Overall, open source LLMs empower more proactive identification of confidentiality risks in AI systems before those flaws manifest at scale. Closed counterparts offer relatively limited transparency into data handling practices.

Commercial Backing and Support

The potential to monetize closed source LLMs incentivizes significant commercial investment for development and maintenance. For example, anticipating lucrative returns from its Azure AI portfolio, Microsoft agreed to multibillion dollar partnerships with OpenAI around GPT models.

In contrast, open source LLMs rely on volunteers allocating personal time for upkeep or grants providing limited-term funding. This resource asymmetry risks the continuity and longevity of open source projects.

However, the barriers to commercialization also free open source communities to focus on scientific progress over profit. And the decentralized nature of open ecosystems mitigates over-reliance on the sustained interest of any single backer.

Ultimately each approach carries trade-offs around resources and incentives. Closed source LLMs enjoy greater funding security but concentrate influence. Open ecosystems promote diversity but suffer heightened uncertainty.

Navigating the Open Source vs Closed Source LLM Landscape

Deciding between open or closed source LLMs calls for matching organizational priorities like customizability, accessibility, and scalability with model capabilities.

For researchers and startups, open source grants more control to tune models to specific tasks. The licensing also facilitates free sharing of insights across collaborators. However, the burden of sourcing training data and infrastructure can undermine real-world viability.

Conversely, closed source LLMs promise sizable quality improvements courtesy of ample funding and data. However, restrictions around access and modifications limit scientific transparency while binding deployments to vendor roadmaps.

In practice, open standards around architecture specifications, model checkpoints, and evaluation data can help offset drawbacks of both approaches. Shared foundations like Google's Transformer or Oxford's REALTO benchmarks improve reproducibility. Interoperability standards like ONNX allow mixing components from open and closed sources.

Ultimately what matters is picking the right tool – open or closed source – for the job at hand. The commercial entities backing closed source LLMs carry undeniable influence. But the passion and principles of open science communities will continue playing a crucial role driving AI progress.

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