Summary
The rapid evolution of large language models has transformed conversational agents, enabling complex human-machine interactions. However, traditional evaluation frameworks often focus on single tasks, failing to capture the dynamic nature of multi-turn dialogues.
This research introduces a dynamic benchmarking framework to assess LLM-based conversational agents through interactions with synthetic users. The framework integrates generative agent simulation to evaluate performance across key dimensions: information extraction, context awareness, and adaptive engagement.
Experimental evaluation within a loan application use case demonstrates the framework’s effectiveness under one-shot and few-shot extraction conditions. Results show that adaptive strategies improve data extraction accuracy, especially when handling ambiguous responses.
Authors: Pietro Alessandro Aluffi (sea.dev), Patrick Zietkiewicz (sea.dev), Marya Bazzi (sea.dev), Matt Arderne (sea.dev), Vladimirs Murevics (sea.dev)