Starting the job hunt is often challenging for new graduates, who face barriers in translating experiences into actionable career profiles due to limited self-awareness and unclear skill mapping. Through formative study with new graduates and early-career professionals, we concluded specific challenges in experience extraction, skill organization, and expressive confidence. Drawing on these insights, we designed CareerCraft, an interactive system that scaffolds the construction of coherent career stories and supports tailored job searching via experience card extraction, guided profile building, and LLM-powered recommendations. In a within-subject evaluation (N=16), participants rated the efficacy of CareerCraft against the baseline condition without the tool in improving profile structuring, clarifying their self-awareness and competencies, and supporting informed job direction choices. Based on the findings, we concluded that CareerCraft offered a promising pathway to career readiness among new graduates to the workforce. We further summarized the design considerations for LLM products emphasizing on users’ self-exploration.

Junyu Luo, Zhizhuo Kou, Liming Yang, Xiao Luo, Jinsheng Huang, Zhiping Xiao, Jingshu Peng, Chengzhong Liu, Jiaming Ji, Xuanzhe Liu, Sirui Han, Ming Zhang, Yike Guo. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Unsupervised Graph Domain Adaptation (UGDA) leverages labeled source domain graphs to achieve effective performance in unlabeled target domains despite distribution shifts. However, existing methods often yield suboptimal results due to the entanglement of causal-spurious features and the failure of global alignment strategies. We propose SLOGAN (Sparse Causal Discovery with Generative Intervention), a novel approach that achieves stable graph representation transfer through sparse causal modeling and dynamic intervention mechanisms. Specifically, SLOGAN first constructs a sparse causal graph structure, leveraging mutual information bottleneck constraints to disentangle sparse, stable causal features while compressing domain-dependent spurious correlations through variational inference. To address residual spurious correlations, we innovatively design a generative intervention mechanism that breaks local spurious couplings through cross-domain feature recombination while maintaining causal feature semantic consistency via covariance constraints. Furthermore, to mitigate error accumulation in target domain pseudo-labels, we introduce a category-adaptive dynamic calibration strategy, ensuring stable discriminative learning. Extensive experiments on multiple real-world datasets demonstrate that SLOGAN significantly outperforms existing baselines.