Personal Carbon Footprint (PCF) Estimation is crucial for raising individual environmental awareness by linking daily activities to their environmental impact. However, existing tools are limited by fragmented scenarios and labor-intensive manual data entry. We present PCCT, an LLM-powered system that combines conversational understanding with emission knowledge grounding for PCF Estimation. We address two key challenges: (1) resolving incomplete activity information across turns through knowledge-guided and context-aware tracking, and (2) accurately mapping emission factors using multi-step LLM inference and vector-based similarity search. The system dynamically combines knowledge-guided activity extraction, and context-aware memory management, generating accurate carbon footprint estimates. We validate the effectiveness with the CarbonDialog-1K benchmark, comprising 1,028 annotated user activity narratives. Experimental results demonstrate that our method outperforms baseline systems in accuracy, while subjective evaluations show superior appropriateness, usability, efficiency, and naturalness.

Data Augmentation (DA) has emerged as a promising solution to address the scarcity of high-quality annotated data in low-resource relation extraction (LRE). Leveraging large language models (LLMs), DA has significantly improved the performance of RE models with considerably fewer parameters. However, existing DA methods struggle with diversity misalignments, as they neglect the diversity required by the model and generate homogeneous augmentations that do not cover the inter-sample and inter-relation variability, leading to suboptimal performance. Inspired by the Observe-Orient-Decide-Act (OODA) framework, which provides a robust theoretical foundation for iterative decision-making under dynamic conditions, we propose an OODA-driven Diverse DA method (ODDA), guiding the data generation and selection process. DDA first observes the RE model’s behavior to select effective demonstrations for LLMs. Next, it orients LLMs towards generating diverse data by replacing schema constraints with attribute constraints. Then ODDA decides on the final augmented dataset with overall diversity from a global search and finally acts to train the RE model. Extensive experiments on three widely-used benchmarks demonstrate that ODDA consistently outperforms state-of-the-art baselines, achieving average F1 improvements of 3.1% across various LRE scenarios while maintaining enhanced model stability.

Reinforcement learning (RL) is a powerful approach to enhance task-oriented dialogue (TOD) systems. However, existing RL methods tend to mainly focus on generation tasks, such as dialogue policy learning (DPL) or response generation (RG), while neglecting dialogue state tracking (DST) for understanding. This narrow focus limits the systems to achieve globally optimal performance by overlooking the interdependence between understanding and generation. Additionally, RL methods face challenges with sparse and delayed rewards, which complicates training and optimization. To address these issues, we extend RL into both understanding and generation tasks by introducing step-by-step rewards throughout the token generation. The understanding reward increases as more slots are correctly filled in DST, while the generation reward grows with the accurate inclusion of user requests. Our approach provides a balanced optimization aligned with task completion. Experimental results demonstrate that our approach effectively enhances the performance of TOD systems and achieves new state-of-the-art results on three widely used datasets, including MultiWOZ2.0, MultiWOZ2.1, and In-Car. Our approach also shows superior few-shot ability in low-resource settings compared to current models.