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Yanming Liu

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3 papers

2 author rows

ECAI Conference 2025 Conference Paper

ASMA-Tune: Unlocking LLMs' Assembly Code Comprehension via Structural-Semantic Instruction Tuning

Xinyi Wang
Jiashui Wang
Jinbo Su
Ke Wang
Peng Chen
Yanming Liu
Long Liu
Xiang Li

Assembly code analysis and comprehension play critical roles in applications like reverse engineering, yet they face substantial challenges due to low information density and a lack of explicit syntactic structures. While traditional masked language modeling (MLM) approaches do not explicitly focus on natural language interaction, emerging decoder-focused large language models (LLMs) demonstrate partial success in binary analysis yet remain underexplored for holistic comprehension. We present Assembly Augmented Tuning (ASMA-Tune), an end-to-end structural-semantic instruction tuning framework that synergizes encoder architecture with decoder-based LLMs through a projector module, where the assembly encoder extracts hardware-level structural features, the projector bridges representations with the semantic space, and the instruction-tuned LLM preserves natural language capabilities. Experimental results demonstrate three key advantages: (1) State-of-the-art performance in assembly comprehension with +39. 7% Recall@1 and +17. 8% MRR improvements over GPT-4-Turbo, (2) Consistent enhancements across base models (24. 6–107. 4% Recall@1 and 15. 2–106. 3% MRR on Qwen2. 5-Coder, Deepseek-Coder and CodeLlama variants), and (3) Superior instruction-following capabilities (41. 5%–118% improvements) with controlled code generation degradation (–8. 9% to –35% across architectures).

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AAAI Conference 2025 Conference Paper

DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models

Yanming Liu
Xinyue Peng
Yuwei Zhang
Xiaolan Ke
Songhang Deng
Jiannan Cao
Chen Ma
Mengchen Fu

Large language models have repeatedly shown outstanding performance across diverse applications. However, deploying these models can inadvertently risk user privacy. The significant memory demands during training pose a major challenge in terms of resource consumption. This substantial size places a heavy load on memory resources, raising considerable practical concerns. In this paper, we introduce DP-MemArc, a novel training framework aimed at reducing the memory costs of large language models while emphasizing the protection of user data privacy. DP-MemArc incorporates side network or reversible network designs to support a variety of differential privacy memory-efficient fine-tuning schemes. Our approach not only achieves about 2.5 times in memory optimization but also ensures robust privacy protection, keeping user data secure and confidential. Extensive experiments have demonstrated that DP-MemArc effectively provides differential privacy-efficient fine-tuning across different task scenarios.

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EAAI Journal 2021 Journal Article

Joint pairwise graph embedded sparse deep belief network for fault diagnosis

Jie Yang
Weimin Bao
Yanming Liu
Xiaoping Li
Junjie Wang
Yue Niu
Jin Li

An enhanced intelligent diagnosis method is proposed based on a joint pairwise graph embedded sparse deep belief network with partial least square fine-tuning (J-PDBN). In this novel framework, the joint pairwise graph embedded sparse deep belief network (DBN) is considered as an unsupervised learning method to realize fast parameters initialization and to extract data features. It combines the advantages of both the pairwise graph and sparse representation, which can preserve the manifold structure of the original data and generate discriminant features. The partial least square (PLS) is used to optimize the parameters to eliminate the gradient diffusion in the supervised learning process. The J-PDBN-based fault diagnosis is implemented by both the unsupervised learning method and PLS fine-tuning, which contributes to better classification capabilities. Finally, gearbox and bearing fault diagnosis experiments are conducted. The results show that the total recognition rates of the proposed method are 93. 65% in the gearbox case and 95. 96% in the bearing case, which are higher than those of other methods. Specifically, the testing accuracy is approximately 10% higher than those of the DBN network for both cases. This validates the effectiveness and superiority of the proposed method.

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