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Prompt

To the extent that enterprises have adopted LLMs, have their preferences differed significantly from that of consumers in terms of the models they use and prefer? Are there LLMs which are little-known among consumers that are seeing popularity among business users?

Output

Enterprises and consumers have shown differing preferences in the adoption of large language models (LLMs), with enterprises increasingly favoring models that offer customization, control, and cost-efficiency, while consumer preferences are more aligned with ease of use and accessibility.

Enterprise vs. Consumer Preferences

  1. Customization and Control: Enterprises often prefer open-source LLMs like Meta's Llama or Mistral because they provide flexibility in fine-tuning and adapting the models to specific business needs. These models allow companies to maintain control over their data, which is crucial for compliance and security in industries like finance, healthcare, and legal services[2][4]. In contrast, consumers tend to gravitate toward proprietary models like OpenAI’s GPT-4 or Google’s Bard due to their ease of access through platforms like ChatGPT or Google Search, without needing to worry about customization or infrastructure[1].
  2. Cost Efficiency: Open-source LLMs such as Llama and Mistral are gaining popularity among enterprises because they offer a lower-cost alternative to proprietary models. These models can be self-hosted, reducing reliance on expensive cloud services tied to proprietary LLMs like GPT-4[2][4]. Consumers, on the other hand, are less concerned with cost at the infrastructure level since they typically interact with these models through free or subscription-based services.
  3. Data Privacy: Enterprises prioritize data privacy and security more than individual consumers. Open-source LLMs allow businesses to deploy models in-house, mitigating risks associated with sharing sensitive data with third-party vendors[2]. Consumers are generally less concerned with these issues when using popular services like ChatGPT or Google Bard.

Several LLMs are gaining traction in enterprises but remain relatively unknown to the general public:

  • Cohere: Cohere’s platform offers enterprise-grade LLMs such as Command and Rerank, which can be fine-tuned for specific business use cases. Cohere emphasizes flexibility by not being tied to a single cloud provider, making it attractive for businesses needing more control over their AI deployments[1].
  • Llama (Meta): Meta’s Llama models, particularly Llama 2 and the upcoming Llama 3, are widely used in enterprise applications due to their open-source nature and competitive performance. These models allow businesses to customize them for tasks such as content generation, customer service automation, and internal knowledge management[2][4]. However, they are less known among consumers because Meta has not marketed them as a direct-to-consumer product.
  • Mistral: Mistral’s smaller-sized models (e.g., Mistral 7B) offer strong performance while being lightweight enough for self-hosting. This makes them ideal for enterprises that need efficient solutions without sacrificing performance. Despite its growing popularity in business applications, Mistral remains relatively obscure among consumers[1][2].
  • Palmyra (Writer): Writer’s Palmyra family of LLMs is designed specifically for enterprise content generation and is used by companies like Accenture and HubSpot. While Writer has open-sourced some of its smaller models, its primary model remains proprietary and is largely unknown outside of enterprise circles[4].

Conclusion

In summary, enterprises have distinct preferences when it comes to adopting LLMs compared to consumers. They prioritize customization, cost efficiency, and data privacy—factors that lead them toward open-source or enterprise-focused models like Cohere, Llama, Mistral, and Palmyra. These models are often lesser-known among general consumers who typically use more accessible proprietary options like GPT-4 or Bard.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.