Scripted Evaluation Framework for Large Language Models: A Controlled Approach to Comparative Analysis
Blog post from Coval
A novel framework for evaluating Large Language Models (LLMs) through controlled scripted interactions has been developed, addressing the limitations of traditional model-to-model conversational evaluations. This approach utilizes structured scenarios with predefined interaction patterns to evaluate LLMs in dynamic conversational settings, focusing on areas such as context awareness, instruction following, and complex function calling. The framework was tested in three distinct scenarios: restaurant service, technical interviews, and sales interactions, revealing significant performance variations among different LLM providers. Results showed that GPT models generally outperformed others, particularly in resolving conflicting information and executing complex tasks, while Gemini exhibited consistent instruction adherence but struggled with context-dependent tasks. The evaluation highlighted the strengths and weaknesses of text and voice implementations, demonstrating that text-based models often performed better, and underscored the framework's effectiveness in providing a structured, comparable assessment of LLM capabilities in real-world conversational contexts.
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