Dynamic benchmarking framework for LLM-based conversational data capture

• URL: http://arxiv.org/abs/2502.04349v1
• Date: Tue, 04 Feb 2025 15:47:47 GMT
• Title: Dynamic benchmarking framework for LLM-based conversational data capture
• Authors: Pietro Alessandro Aluffi, Patrick Zietkiewicz, Marya Bazzi, Matt Arderne, Vladimirs Murevics,
• Abstract summary: This paper introduces a benchmarking framework to assess large language models (LLMs) It integrates generative agent simulation to evaluate performance on key dimensions: information extraction, context awareness, and adaptive engagement. Results show that adaptive strategies improve data extraction accuracy, especially when handling ambiguous responses.
• Score: 0.0
• License:
• Abstract: The rapid evolution of large language models (LLMs) has transformed conversational agents, enabling complex human-machine interactions. However, evaluation frameworks often focus on single tasks, failing to capture the dynamic nature of multi-turn dialogues. This paper 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 on key dimensions: information extraction, context awareness, and adaptive engagement. By simulating various aspects of user behavior, our work provides a scalable, automated, and flexible benchmarking approach. 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. Future work will extend its applicability to broader domains and incorporate additional metrics (e.g., conversational coherence, user engagement). This study contributes a structured, scalable approach to evaluating LLM-based conversational agents, facilitating real-world deployment.

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