The primary goal of Motivational Interviewing (MI) is to help clients build their own motivation for behavioral change. To support this in dialogue systems, it is essential to guide large language models (LLMs) to generate counselor responses aligned with MI principles. By employing a schema-guided approach, this study proposes a method for updating multi-frame dialogue states and a strategy decision mechanism that dynamically determines the response focus in a manner grounded in MI principles. The proposed method was implemented in a dialogue system on two different datasets and evaluated through a user study. Results showed that the proposed method successfully generates responses aligned with MI principle and frequently asks questions to elicit change talk.

The widespread use of large language models (LLMs) has increased the risk of leaking personally identifiable information (PII), making PII detection increasingly important. PII detection must process large text volumes efficiently, but labeled PII text is difficult to collect, share, and redistribute for privacy reasons. We aim to develop rubric-guided contrastive data synthesis for training lightweight PII text detectors from a few labeled seeds and to determine, through comparative experiments on PII benchmark datasets, whether the proposed method improves PII text detector classification performance. From about ten labeled examples, our method induces explicit criteria for distinguishing the two labels (rubrics), generates contrastive Sensitive/Non-sensitive sentence pairs, verifies and deduplicates them, and trains RoBERTa. Experiments on SPeDaC1 and SPY with Qwen3.5-9B and Gemma-4-26B-A4B showed that our method outperformed four baselines in all four settings and significantly improved Macro F1 over the Self-Instruct-style baseline in three of four settings. Overall, we developed rubric-guided contrastive data synthesis for training lightweight PII text detectors from a few labeled seeds and confirmed through comparative experiments on PII benchmark datasets that the proposed method improves classification performance. Our code is available at https://github.com/ekunish/emnlp2026-code.