Dynamic benchmarking framework for LLM-based conversational data capture
This provides a scalable evaluation framework for developers and researchers working on conversational AI, though it is incremental in extending existing benchmarking approaches to multi-turn scenarios.
The paper tackles the problem of evaluating LLM-based conversational agents in dynamic multi-turn dialogues by introducing a benchmarking framework that uses synthetic users to assess information extraction, context awareness, and adaptive engagement. Results show adaptive strategies improve data extraction accuracy, particularly for ambiguous responses, as demonstrated in a loan application use case.
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.