Generative retrieval enhances retrieval effectiveness by generating natural language represented document identifiers. However, current methods often struggle with two major challenges: limited identifier quality and insufficient query-document interaction, leading to limited retrieval performance. To tackle these challenges, we propose a novel generative retrieval framework integrated with semantic-aligned multi-layer identifiers and reinforcement learning. To improve identifier quality, we design a prompt-driven multi-task learning strategy to generate three types of hierarchical identifiers: summary, keyword, and pseudo-query, to capture multi-granularity document semantics. Furthermore, we adopt supervised fine-tuning to integrate these identifiers. To improve query-document interaction, we devise a multi-view ranking fusion mechanism that combines retrieval results across multi-layer identifiers. We further employ a GRPO-based reinforcement learning based on dense similarity rewards and a difficulty-aware negative sampling strategy to optimize the generated identifiers. Experiments on multiple benchmark datasets show that our framework significantly outperforms existing generative retrieval methods, offering a promising solution for building more effective and semantically aligned retrieval systems. The code for our model is publicly available at https://github.com/yicentian02/GRAM-RL.

Multimodal hate detection aims to identify hate content across multiple modalities for promoting a harmonious online environment. Despite promising progress, three critical challenges, the absence of implicit hateful cues, the cross-modal-induced hate, and the diversity of hate target groups, inherent in the multimodal hate detection task, have been overlooked. To address these challenges, we propose a hypergraph-based prompting fusion model. Our model first uses tailored prompts to infer implicit hateful cues. It then introduces hyperedges to capture cross-modal-induced hate and applies a diversity-oriented hyperedge expansion strategy to account for different hate target groups. Finally, hypergraph convolution fuses diverse hateful cues, enhancing the exploration of cross-modal hate and targeting specific groups. Experimental results on two benchmark datasets show that our model achieves state-of-the-art performance in multimodal hate detection.

The rapid development of large language models has greatly advanced human-computer dialogue research. However, applying these models to specialized fields like maternity and infant care often leads to subpar performance due to a lack of domain-specific datasets. To address this problem, we have created MicDialogue, a Chinese dialogue dataset for maternity and infant care. MicDialogue involves a wide range of specialized topics, including gynecological health, pediatric care, pregnancy preparation, emotional counseling and other related topics. This dataset is curated from two types of Chinese social media: short videos and blog posts. Short videos capture real-time interactions and pragmatic dialogue patterns, while blog posts offer comprehensive coverage of various topics within the domain. We have also included detailed annotations for topics, diseases, symptoms, and causes, enabling in-depth research. Additionally, we developed a knowledge-driven benchmark model using LLM-based prompt learning and multiple knowledge graphs to address diverse dialogue topics. Experiments validate MicDialogue's usability, providing benchmarks for future research and essential data for fine-tuning language models in maternity and infant care.