IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data
This addresses the data scarcity problem for researchers and developers working on personalized dialogue systems, though it is incremental as it builds on existing evaluation methods.
The paper tackles the challenge of evaluating implicit personalization in dialogue systems by proposing a synthetic data generation approach, introducing the IP-Dialog benchmark with 10 tasks and 12 user attribute types, and developing an evaluation framework with four metrics, which proves reliable in experiments.
In modern dialogue systems, the ability to implicitly infer user backgrounds from conversations and leverage this information for personalized assistance is crucial. However, the scarcity of high-quality data remains a fundamental challenge to evaluating and improving this capability. Traditional dataset construction methods are labor-intensive, resource-demanding, and raise privacy concerns. To address these issues, we propose a novel approach for automatic synthetic data generation and introduce the Implicit Personalized Dialogue (IP-Dialog) benchmark along with a training dataset, covering 10 tasks and 12 user attribute types. Additionally, we develop a systematic evaluation framework with four metrics to assess both attribute awareness and reasoning capabilities. We further propose five causal graphs to elucidate models' reasoning pathways during implicit personalization. Extensive experiments yield insightful observations and prove the reliability of our dataset.