Author name cluster

Bolin Shen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

2 author rows

AAAI Conference 2026 Conference Paper

Query-Efficient Domain Knowledge Stealing Against Large Language Models

Zhengao Li
Xiaopeng Yuan
Bolin Shen
Kien Le
Haohan Wang
Xugui Zhou
Shangqian Gao
Yushun Dong

Large language models (LLMs) concentrate substantial knowledge in specialized domains due to extensive pretraining and instruction tuning, and they are now central to commercial and scientific practice. Yet access is usually limited to costly, rate-limited interfaces, which motivates methods that can extract targeted domain knowledge with minimal querying effort. A further challenge is that the target domain may be unknown in advance, so naive or generic prompts waste queries and fail to expose the underlying concepts and relations that structure the domain. In this work, we introduce a query-efficient approach for domain-specific knowledge stealing from black-box language models. Rather than issuing random questions or generic templates, our framework performs self-directed exploration that lets the model find the direction and mine domain knowledge by itself. Starting from a small and diverse seed, it discovers salient domain entities and induces their relations through structured question families that elicit definitional, functional, and compositional information. A feedback-driven controller analyzes the errors and uncertainty of the extracted surrogate model and uses this signal to refine subsequent queries, all without relying on prior domain knowledge or external resources. We evaluate the method in two expert-centric settings, medicine and finance, and observe consistently better performance while requiring significantly fewer queries.

PDF Details DOI

ICML Conference 2025 Conference Paper

CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Zebin Wang
Menghan Lin
Bolin Shen
Ken Anderson
Molei Liu
Tianxi Cai
Yushun Dong

Graph Neural Networks (GNNs) have demonstrated remarkable utility across diverse applications, and their growing complexity has made Machine Learning as a Service (MLaaS) a viable platform for scalable deployment. However, this accessibility also exposes GNN to serious security threats, most notably model extraction attacks (MEAs), in which adversaries strategically query a deployed model to construct a high-fidelity replica. In this work, we evaluate the vulnerability of GNNs to MEAs and explore their potential for cost-effective model acquisition in non-adversarial research settings. Importantly, adaptive node querying strategies can also serve a critical role in research, particularly when labeling data is expensive or time-consuming. By selectively sampling informative nodes, researchers can train high-performing GNNs with minimal supervision, which is particularly valuable in domains such as biomedicine, where annotations often require expert input. To address this, we propose a node querying strategy tailored to a highly practical yet underexplored scenario, where bulk queries are prohibited, and only a limited set of initial nodes is available. Our approach iteratively refines the node selection mechanism over multiple learning cycles, leveraging historical feedback to improve extraction efficiency. Extensive experiments on benchmark graph datasets demonstrate our superiority over comparable baselines on accuracy, fidelity, and F1 score under strict query-size constraints. These results highlight both the susceptibility of deployed GNNs to extraction attacks and the promise of ethical, efficient GNN acquisition methods to support low-resource research environments. Our implementation is publicly available at https: //github. com/LabRAI/CEGA.

Details