Guidelines for Evaluating Models Powered by Large Language Models (LLMs)

Introduction:

In the era of advanced artificial intelligence, Large Language Models (LLMs) play a pivotal role in various applications, from generating human-like text to performing complex reasoning tasks. This article establishes guidelines for evaluating models incorporating or interacting with LLM outputs, ensuring their effectiveness, reliability, and alignment with intended purposes.

Establishing the purpose of the model:

In evaluating models that incorporate Large Language Models (LLMs), it is crucial to understand the specific task the model is designed to address. This foundational step ensures that subsequent evaluations align with the intended purpose and application area of the LLM. For example, the model’s task could involve generating human-like text, extracting specific information from unstructured data, mathematical calculations or logical reasoning described in natural language, etc. Each task leverages a different ability of LLMs therefore the process of identifying the task and delineating how the LLM is employed forms the foundation for an effective evaluation.

Mapping the intended purpose to quantifiable mathematical metrics:

The translation of a model's purpose into mathematical metrics for objective analysis is a crucial step in evaluating model performance. This process involves the identification of measures that can benchmark model performance based on statistical or mathematical formulas, thereby providing a quantitative foundation for assessment.

For instance, in the case of an LLM-powered model tasked with binary classification, several established metrics like the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Sensitivity, Specificity, etc. come into play.

By leveraging these metrics, evaluators can establish a measure of the model's performance, identifying strengths and areas for improvement. This mathematical translation facilitates an objective evaluation of model performance and also enables the comparison of different models or iterations of the same model over time, guiding continuous refinement and optimization.

Evaluating model structure:

Evaluating the structure of a model that incorporates a Large Language Model (LLM) necessitates a deep dive into the specifics of the LLM's application within the model, alongside an understanding of its training regimen and selection rationale. Key questions guiding this evaluation include:

• What specific Large Language Model (LLM) is used to power the model, and what type of training sample was the model trained on?
• What criteria were used to select the LLM for the model, were any alternate LLMs considered, and what tasks was the LLM originally trained on?
• Where are the LLM's components, such as its weights, basis vectors, and tokenizer, stored, and are they hosted locally in a secure environment? 
• Is there a Retrieval-Augmented Generation (RAG) setup as part of the model, and if so, how is it configured and utilized?
• What similarity measure is used in the RAG setup, and what benchmarking was performed to select this particular measure?
• What prompts or sequence of prompts are used to interact with the model, and what testing was conducted to select these prompts?
• How is context built for each prompt or interaction with the LLM in the RAG setup, and does the RAG pull context for each prompt independently, or do previous interactions and responses contribute to the context?
• What controls are in place to ensure the Large Language Model's (LLM) output is consistent, accurate, free from bias, and minimizes hallucinations?
• Was the LLM fine-tuned, and if so, what task was it fine-tuned on, what were the training samples used, and how was its performance evaluated?
• Is there a real-time feedback loop incorporated into the model, allowing human feedback to play a role in improving the outputs once the LLM is deployed?
• What controls are established to evaluate the performance of the LLM over time, and what is the typical maintenance cycle for the model (e.g., re-evaluation every 1, 3, 6 months)?
• How does the model adapt to and incorporate the latest LLM releases, especially if the initially used open-source LLM version has been revised and replaced by a newer version?
• What are the controls in place to check for ethical considerations and bias mitigation?
• How are the users and stakeholders informed, in understandable terms, of the model’s transparency and ability to arrive at its outputs

Ethical Considerations:

Given that LLMs are trained on vast internet datasets, they risk encapsulating and perpetuating biases. It is crucial to implement quantitative and qualitative measures to identify and mitigate biases, ensuring the model's decisions and recommendations are fair. Additionally, users and stakeholders should be informed about the model’s transparency and decision-making processes in understandable terms

Conclusion:

Evaluating models that incorporate LLMs is a multi-faceted process that ensures their reliability and effectiveness. By establishing clear purposes, translating them into quantifiable metrics, and thoroughly analyzing the model structure and ethical considerations, one can enhance the performance, transparency, explainability, and trustworthiness of LLM-powered models.