What determines pension insurance participation in China?: triangulation and the intertwined relationship among employers, employees and the government

The current study draws on the Advocacy Coalition Framework to examine what determines employees’ pension participation in China. For the purpose of exploring which employees actually receive pension coverage and why, econometric analysis was conducted with China’s Employer–Employee Matched Survey data (N = 3412). A variety of both individual factors, ranging from age and Hukou status to job characteristics, and macro factors, including interprovincial migration and level of economic development, are all found to predict insurance coverage. Qualitative research results contextualize these findings by discussing the often ambivalent and triangulated relations among employers, employees and government. These three groups primarily use shared core policy beliefs to structure their interactions in the form of advocacy coalitions. Various types of cross-coalition interaction, including negotiation, cooperation and conflict, are examined. These findings carry both theoretical and policy implications

Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation

Existing controllable dialogue generation work focuses on the single-attribute control and lacks generalization capability to out-of-distribution multiple attribute combinations. In this paper, we explore the compositional generalization for multi-attribute controllable dialogue generation where a model can learn from seen attribute values and generalize to unseen combinations. We propose a prompt-based disentangled controllable dialogue generation model, DCG. It learns attribute concept composition by generating attribute-oriented prompt vectors and uses a disentanglement loss to disentangle different attributes for better generalization. Besides, we design a unified reference-free evaluation framework for multiple attributes with different levels of granularities. Experiment results on two benchmarks prove the effectiveness of our method and the evaluation metric.Comment: ACL 2023 Main Conferenc

Vectorial structure of a hard-edged-diffracted four-petal Gaussian beam in the far field

Based on the vector angular spectrum method and the stationary phase method and the fact that a circular aperture function can be expanded into a finite sum of complex Gaussian functions, the analytical vectorial structure of a four-petal Gaussian beam (FPGB) diffracted by a circular aperture is derived in the far field. The energy flux distributions and the diffraction effect introduced by the aperture are studied and illustrated graphically. Moreover, the influence of the f-parameter and the truncation parameter on the nonparaxiality is demonstrated in detail. In addition, the analytical formulas obtained in this paper can degenerate into un-apertured case when the truncation parameter tends to infinity. This work is beneficial to strengthen the understanding of vectorial properties of the FPGB diffracted by a circular aperture

Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task

With the increasing capabilities of large language models (LLMs), these high-performance models have achieved state-of-the-art results on a wide range of natural language processing (NLP) tasks. However, the models' performance on commonly-used benchmark datasets often fails to accurately reflect their reliability and robustness when applied to real-world noisy data. To address these challenges, we propose a unified robustness evaluation framework based on the slot-filling task to systematically evaluate the dialogue understanding capability of LLMs in diverse input perturbation scenarios. Specifically, we construct a input perturbation evaluation dataset, Noise-LLM, which contains five types of single perturbation and four types of mixed perturbation data. Furthermore, we utilize a multi-level data augmentation method (character, word, and sentence levels) to construct a candidate data pool, and carefully design two ways of automatic task demonstration construction strategies (instance-level and entity-level) with various prompt templates. Our aim is to assess how well various robustness methods of LLMs perform in real-world noisy scenarios. The experiments have demonstrated that the current open-source LLMs generally achieve limited perturbation robustness performance. Based on these experimental observations, we make some forward-looking suggestions to fuel the research in this direction.Comment: Accepted at NLPCC 2023 (Oral Presentation