Yaming Yang

Yichen Ouyang, Lu Wang, Fangkai Yang et al.

Query generation is a critical task for web search engines (e.g. Google, Bing) and recommendation systems. Recently, state-of-the-art query generation methods leverage Large Language Models (LLMs) for their strong capabilities in context understanding and text generation. However, they still face challenges in generating high-quality queries in terms of inferring user intent based on their web search interaction history. In this paper, we propose Token-level Proximal Policy Optimization (TPPO), a noval approach designed to empower LLMs perform better in query generation through fine-tuning. TPPO is based on the Reinforcement Learning from AI Feedback (RLAIF) paradigm, consisting of a token-level reward model and a token-level proximal policy optimization module to address the sparse reward challenge in traditional RLAIF frameworks. To evaluate the effectiveness and robustness of TPPO, we conducted experiments on both open-source dataset and an industrial dataset that was collected from a globally-used search engine. The experimental results demonstrate that TPPO significantly improves the performance of query generation for LLMs and outperforms its existing competitors.

Ziyu Zheng, Yaming Yang, Ziyu Guan et al.

Real-world networks usually have a property of node heterophily, that is, the connected nodes usually have different features or different labels. This heterophily issue has been extensively studied in homogeneous graphs but remains under-explored in heterogeneous graphs, where there are multiple types of nodes and edges. Capturing node heterophily in heterogeneous graphs is very challenging since both node/edge heterogeneity and node heterophily should be carefully taken into consideration. Existing methods typically convert heterogeneous graphs into homogeneous ones to learn node heterophily, which will inevitably lose the potential heterophily conveyed by heterogeneous relations. To bridge this gap, we propose Relation-Aware Separation of Homophily and Heterophily (RASH), a novel contrastive learning framework that explicitly models high-order semantics of heterogeneous interactions and adaptively separates homophilic and heterophilic patterns. Particularly, RASH introduces dual heterogeneous hypergraphs to encode multi-relational bipartite subgraphs and dynamically constructs homophilic graphs and heterophilic graphs based on relation importance. A multi-relation contrastive loss is designed to align heterogeneous and homophilic/heterophilic views by maximizing mutual information. In this way, RASH simultaneously resolves the challenges of heterogeneity and heterophily in heterogeneous graphs. Extensive experiments on benchmark datasets demonstrate the effectiveness of RASH across various downstream tasks. The code is available at: https://github.com/zhengziyu77/RASH.

Ziyu Zheng, Yaming Yang, Ziyu Guan et al.

The "pre-train, prompt" paradigm has successfully evolved in representation learning. While current prompt-tuning methods often introduce learnable prompts, they predominantly treat prompts as isolated, independent components across different network layers. This overlooks the complex and synergistic higher-order interactions that exist between prompts at various hierarchical depths, consequently limiting the expressive power and semantic richness of the prompted model. To address this fundamental gap, we propose a novel framework that explicitly models the Higher-order Interactions of Multi-layer Prompt. Our approach conceptualizes prompts from different layers not as separate entities, but as a cohesive system where their inter-relationships are critical. We design an innovative interaction module that captures these sophisticated, non-linear correlations among multi-layer prompts, effectively modeling their cooperative effects. This allows the model to dynamically aggregate and refine prompt information across the network's depth, leading to a more integrated and powerful prompting strategy. Extensive experiments on eight benchmark datasets demonstrate that our method, by leveraging these higher-order interactions, consistently surpasses state-of-the-art prompt-tuning baselines. The performance advantage is particularly pronounced in few-shot scenarios, validating that capturing the intricate interplay between multi-layer prompts is key to unlocking more robust and generalizable representation learning.