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05.14.24 —

A Practitioner’s Guide to Selecting Large Language Models for Your Business Needs

Summary: 

 

  • We share a guide we’ve developed that provides a roadmap for businesses to effectively leverage Large Language Models (LLMs), emphasizing their potential to revolutionize operations in the AI-driven business landscape.
  • The guide outlines steps to identify suitable use cases for LLM integration, including areas for automation, data availability assessment, scalability considerations, and common applications such as document summarization and customer support chatbots.
  • We highlight the factors businesses should consider in selecting the right LLM, like task-specific capabilities, language support, fine-tuning abilities, computational requirements, and safety features along with benchmark evaluations of various models’ performance.

As artificial intelligence (AI) continues to shape the business landscape, understanding and effectively utilizing large language models (LLMs) can be a game-changer for your company. Whether you are just beginning your journey with LLMs or looking to deepen your existing knowledge, we have developed a guide that explores the topic.

In this blog, we’ll provide a sneak peak into what we discuss within that guide, which aims to help you develop a clear roadmap for selecting the most suitable LLM for your specific business needs and maximizing its potential.

Evaluating Business Needs and Use Cases

When integrating LLMs into your business operations, it is crucial to identify the areas where these advanced AI tools can have the most significant impact. LLMs’ versatility allows for applications in various sectors and functions, but success lies in aligning them with the right use cases. In this section, we guide you through identifying potential use cases for LLMs and present case studies to illustrate their practical application.

Identifying Use Cases

To identify your business’s most beneficial use cases for LLMs, assess your current challenges and opportunities. Analyze areas where your organization could improve efficiency, accuracy, or innovation. Look for tasks that involve manual language processing, such as reading, writing, or interpreting text. These areas present opportunities for LLM integration.

Next, identify areas for automation and enhancement. LLMs excel in automating routine language-based tasks and enhancing complex ones. Consider areas like customer support, where LLMs can handle routine queries, freeing up human agents for more complex issues.

Evaluate the availability and quality of data needed for successful LLM implementation. LLMs rely on substantial and relevant data to deliver accurate results. Assess the data your business generates, such as customer interactions, product descriptions, or research reports.

Finally, consider scalability and impact. Focus on use cases where LLMs can scale operations or significantly improve outcomes. Content creation, analytics, and personalized recommendation systems are examples of use cases where LLMs can generate high-quality content or process vast amounts of text data for insights. The guide highlights the common use cases we’ve seen for companies applying LLMs to their business and typical application features found with most LLMs.

Download the guide here to learn more about how to identify the LLM application use cases

Choosing the Right LLM

Choosing the right LLM for your needs involves considering various factors impacting the model’s effectiveness, scalability, and applicability. This section will provide a structured approach to making this decision.

Some factors that you should consider when selecting an LLM include:

  • Task-Specific Capabilities
  • Language Support
  • Fine-Tuning Abilities
  • Extended Context Handling
  • Computational Requirements
  • Content Moderation and Hallucination Prevention
  • Latency and Response Times
  • Cost and Licensing

LLM Benchmarks

To evaluate LLMs effectively, it is essential to consider benchmark metrics that assess model performance. While popular benchmarks like SQuAD for reading comprehension and BLEU for translation accuracy have been traditionally used, new benchmarks have emerged to evaluate LLMs comprehensively. In the guide, we review the following benchmarks to evaluate LLMs for specific application needs:

  • Knowledge: MMLU and TriviaQA
  • Reasoning: HellaSwag and WinoGrande
  • Comprehension: DROP and Race-h
  • Coding: HumanEval and MBPP
  • Math: GSM8k and MATH

Comparing Models

To select the right LLM, comparing different models based on their performance on relevant benchmarks is essential. While studies have evaluated various models against different benchmarks, it is crucial to consider the limitations of each study. Here are the LLMs we review through findings from recent studies and technical reports to gain insights into their performance:

  • OpenAI GPT-4 and GPT-3.5 Turbo
  • Google Gemini 1.5 Pro and 1.0 Ultra
  • Meta Llama-3
  • Anthropic Claude 3 Opus, Sonnet, and Haiku
  • Mistral.AI Mixtral 8x7B, 8x22B, and Mistral Large
  • X.AI Grok 1and Grok 1.5

LLM Safety

Safety is a critical aspect of LLM applications, and it is essential to consider the safety capabilities of different models. While safety remains one of the least studied aspects of LLM capabilities, recent studies have shed light on certain dimensions of LM safety, including truthfulness, toxicity, and bias.

Conducting safety testing and tuning specific to your model applications is crucial, especially when non-English communication is involved. Ongoing empirical research is necessary for a more comprehensive understanding of LLM safety. In the absence of that research, we encourage the ethical and responsible use of AI technology by following the guardrails of trust, transparency, safety and compliance, and empowerment outlined by our AI for Good standards. 

Conclusion

In conclusion, selecting the right LLM for your business requires thoroughly evaluating use cases, considering factors such as task-specific capabilities and language support, and comparing models based on relevant benchmarks. Safety is also a crucial aspect to consider in LLM applications.

Download this comprehensive guide to navigate the complex world of LLMs and harness their potential to revolutionize your business operations.