How to pick the best Large Language Model (LLM).

When selecting an LLM for your Generative AI implementation, clearly defining your objectives and the model's intended use is crucial. Take time to understand the business problem you want the LLM to solve and how it aligns with your strategic goals. A wide variety of LLMs are available, and each excels at specific tasks. Therefore, it's important to understand how the model solves your problem, whether generating insights, new content, product recommendations, or translations.

Below are the key factors you may need to consider when selecting an LLM.

1.?Model Size and Capabilities.

When selecting Large Language Models (LLMs), a critical factor to consider is the size and capabilities of the model(s) in question. Typically, models trained on more extensive datasets demonstrate superior performance. However, it's important to consider that these larger models require significantly more computational resources and may only be suitable for some business applications or budgets. Therefore, it's crucial for organizations to carefully match their performance expectations and resource availability with the appropriate model size to ensure an optimal balance between capability and cost.?

Below are examples of different large language models and some of their key differences:

• ChatGPT

o?? A Generative pre-trained transformer excelling in a wide range of natural language processing tasks, including text generation, translation, summarization, and more.

• AI21 Labs

o?The primary goal of this model is to assist users in generating human-like text for various purposes, such as creative writing, content creation, and conversational interfaces.

• Cohere

o?? Offers various models ranging from text generation to summarization and code analysis

• Llama 2

o?? Developed by Meta, this model is part of the open-source large language model offered for free. Meta aim to democratize LLMs and provide everyday developers to experiment with them.

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2.?Pre-Training Task.

The initial pre-training objective of a model crucially determines its efficacy across a range of subsequent applications. For instance, models pre-trained using the masked language modelling technique tend to outperform in natural language understanding tasks. Conversely, models utilizing an encoder-decoder framework are better suited for text generation tasks like report writing. Therefore, selecting a pre-trained model with objectives closely aligned with your specific task requirements can enhance performance outcomes.

?3.?Fine-Tuning Requirements.

Certain projects might necessitate the customization of a model through fine-tuning on a particular domain or dataset for optimal performance. It is important to evaluate if a model supports straightforward custom fine-tuning or if it delivers effective performance straight out of the box. A good example is the out-of-the-box performance of a model pre-trained on a large and diverse dataset, showcasing strong performance across general language tasks.

However, it may not be specifically tailored to the nuances and vocabulary commonly found in e-commerce reviews. A custom fine-tuned model specifically designed for sentiment analysis in e-commerce and pre-trained on a dataset containing a wide range of product reviews will gain domain-specific knowledge and understanding of the language commonly used in customer feedback. Additionally, the resources and technical expertise required for fine-tuning should also be considered when deciding on the most appropriate approach.

4.?Inference Speed and Costs.

For applications demanding real-time responses or those operating at a large scale, the efficiency and expense of model inference become critical factors. A good example is a sophisticated deep learning model trained on a massive dataset capable of providing highly personalized product recommendations based on users' browsing and purchase history.

However, the computational requirements for inference are substantial. The model involves complex computations, and its real-time inference may demand considerable processing power and resources.

Large Language Models (LLMs) differ in their processing speed and cost, which are influenced by their size, architecture, and how they are deployed. Benchmarking models against your specific latency requirements and financial constraints is advisable to identify one that aligns with your needs.

?5.?Lifecycle and Support.

Large Language Model (LLM) technology is evolving swiftly. To mitigate the risk of adopting technology that may soon become obsolete, it's important to consider the development roadmap of the model provider and the level of support it offers.

For example, consider a model provider that regularly releases updated versions of its LLM, introducing improvements in performance, efficiency, and capabilities. An active development roadmap, supported by a history of meaningful updates, indicates a commitment to refining and expanding the model's capabilities over time. This ensures that your organization benefits from the latest features and guards against the risk of technological obsolescence.

6.?Licensing.

Licensing plays a key role in protecting intellectual property. It is essential for organizations aiming to deploy AI models in business or commercial settings to select a model whose licensing terms align with their specific use cases. A deep comprehension of the licensing conditions for AI models is crucial to prevent the selection of a model intended mainly for research when the goal is commercial utilization.

For example, consider a scenario where an organization plans to integrate an AI model into its proprietary software product for commercial sale. Selecting a model released under an open-source license that aligns with commercial use cases would be crucial. Conversely, a proprietary license might be more suitable if the organization seeks to protect its investment in the model and maintain exclusivity over its commercial applications.?

7.?Provider offerings

Many vendors offer models that can be downloaded and deployed directly on your infrastructure. Conversely, some providers offer LLMs through a service model, where the model's inner workings and training specifics remain proprietary, e.g., AWS Bedrock. This setup enables users to submit queries or requests and incur costs on a pay-per-use basis. For instance, GPT-4 are available as a service. In contrast, Llama 2 can be directly downloaded and integrated into your system, providing the flexibility to choose a model that best fits your requirements.?

Evaluating Performance.

When evaluating an LLM's performance, there are a range of factors to consider, for example.

• Response times: The speed at which the LLM generates responses is crucial to user experience. A slow response time can lead to frustration and hinder the effectiveness of your application. Consider an AI-powered chatbot integrated into a customer support application. Users expect quick responses to their inquiries. If the LLM behind the chatbot has a fast response time, users will experience a seamless and efficient interaction. However, a slow response time might frustrate users, negatively impacting their experience and the effectiveness of the customer support system.
• Processing capabilities: Understanding the LLM's processing capabilities is essential. You'll want to know if it can handle large volumes of data and complex queries without compromising performance. Imagine a natural language processing system designed for analyzing customer feedback across a large e-commerce platform. The LLM's processing capabilities would be tested when dealing with vast amounts of diverse textual data. An efficient LLM should handle this data at scale, extracting meaningful insights and patterns from complex queries without compromising performance.
• Accuracy: The accuracy of the LLM's responses is vital to ensure that the information provided is reliable and trustworthy. High response accuracy is particularly important for applications that require precise and correct answers. In a medical diagnosis application powered by an LLM, accuracy is paramount. If a user inputs symptoms, the LLM's responses must be accurate to provide reliable health information. A high level of accuracy is critical in applications where precision and correctness are vital, such as medical diagnoses, legal document analysis, or financial forecasting.
• Ability to handle bias: Bias in language models is a significant concern. Evaluating how well the LLM can handle bias and minimizing potential biases is critical to ensure fair and ethical outcomes. Let’s say you have an LLM used in a recruitment tool that screens resumes and applications. The ability to handle bias is crucial to ensure fair and equitable evaluations of candidates. The LLM should be evaluated on its capacity to recognize and minimize biases, preventing discrimination based on factors such as gender, race, or ethnicity. An LLM that effectively addresses bias contributes to ethical and unbiased decision-making processes.?

To evaluate the performance of an LLM effectively, it's important to establish evaluation metrics that align with your specific use case and goals. Below are some metrics to consider:

• Completeness: How well does the LLM provide complete answers or responses?
• Accuracy: How accurate are the LLM's responses? Do they align with the expected or correct answers?
• Politeness: Does the LLM generate responses that are polite and considerate?
• Brevity: Does the LLM provide concise responses without unnecessary verbosity?
• Relevance: How relevant are the LLM's responses to a given query or prompt?
• Generalization: How well does the LLM generalize its learned patterns to unseen or new data, indicating its adaptability to various scenarios beyond the training dataset?
• Perplexity: How well does the LLM predict sequences of words or tokens, and what is its level of perplexity as a measure of language model performance?
• Fairness and Bias: To what extent does the LLM exhibit fairness in its responses, avoiding biases against specific groups or demographics?
• Robustness and Reliability: How robust is the LLM in handling diverse inputs and scenarios, and how reliable are its predictions under different conditions?
• Throughput: What is the processing speed and efficiency of the LLM in terms of throughput, particularly in real-time applications where rapid responses are critical?
• Toxicity: Does the LLM generate responses that contain offensive or harmful content?

Analyzing the evaluation results will help identify areas for improvement and areas where the LLM excels. It's important to iterate and fine-tune an LLM based on this analysis to meet your desired standards and requirements. Benchmarking the LLM against other models in the field can also provide valuable insights and help ensure that the selected LLM stands out regarding value and performance.?

Cost.

When choosing an LLM, it's important to consider the costs involved. Below are some cost factors to consider:

• Pricing structure: Different LLM providers have varying pricing models. Some may charge based on usage, while others may offer subscription-based plans or require one-time licensing fees. For example, OpenAI’s pricing varies based on the model and usage. For instance, GPT-4 costs $0.03 for 1K tokens (input)/ $0.06 for 1K token (output) while AWS bedrock on-demand pricing is $0.00163 for 1K input tokens and $0.00551 for 1K output tokens. It's crucial to understand the LLM pricing structure and determine which model aligns with your budget and usage requirements.
• Expected usage: Calculate the expected usage of the LLM to estimate the associated costs. Consider factors such as the number of queries or requests your application will make to the LLM and the number of tokens used for input/output. By assessing your expected usage, you can choose an LLM that fits within your expected budget.
• Value proposition: Evaluate the LLM's value proposition in relation to its cost. Consider the benefits it brings to your application, such as improved user experience, increased efficiency, or enhanced functionality. Assess whether the cost of the LLM aligns with the business value it provides to ensure a worthwhile investment.
• Privacy and security implications: If your application involves handling sensitive user information, it's crucial to consider the privacy and security implications of the chosen LLM. Ensure the LLM provider follows strict privacy and security protocols to protect user data. Failure to do so could result in potential risks or legal issues.?

Choosing an LLM can be complex so get informed.

The process of implementing generative AI can be challenging due to the wide variety of LLMs available in the market. Choosing the most appropriate LLM that aligns with your business needs can be a complex and overwhelming. It requires a thorough understanding of the different LLMs, their features, and how they can be integrated into your existing systems.?

Ultimately, the decision of which LLM to choose is a personal one, but by following these guidelines, you can make an informed decision that will benefit your business in the long run. The key is to take your time and pick an LLM that will help you excel in your endeavours.

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