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Tianlong Wang

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3 papers

2 author rows

ICLR Conference 2025 Conference Paper

DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing

Xinyu Ma
Yifeng Xu
Yang Lin
Tianlong Wang
Xu Chu
Xin Gao
Junfeng Zhao 0001
Yasha Wang

We introduce DRESS, a novel approach for generating stylized large language model (LLM) responses through representation editing. Existing methods like prompting and fine-tuning are either insufficient for complex style adaptation or computationally expensive, particularly in tasks like NPC creation or character role-playing. Our approach leverages the over-parameterized nature of LLMs to disentangle a style-relevant subspace within the model's representation space to conduct representation editing, ensuring a minimal impact on the original semantics. By applying adaptive editing strengths, we dynamically adjust the steering vectors in the style subspace to maintain both stylistic fidelity and semantic integrity. We develop two stylized QA benchmark datasets to validate the effectiveness of DRESS, and the results demonstrate significant improvements compared to baseline methods such as prompting and ITI. In short, DRESS is a lightweight, train-free solution for enhancing LLMs with flexible and effective style control, making it particularly useful for developing stylized conversational agents. Codes and benchmark datasets are available at https://github.com/ArthurLeoM/DRESS-LLM.

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NeurIPS Conference 2025 Conference Paper

Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation

Weibin Liao
Tianlong Wang
Yinghao Zhu
Yasha Wang
Junyi Gao
Liantao Ma

Medical Lay Language Generation (MLLG) plays a vital role in improving the accessibility of complex scientific content for broader audiences. Recent literature to MLLG commonly employ parameter-efficient fine-tuning methods such as Low-Rank Adaptation (LoRA) to fine-tuning large language models (LLMs) using paired expert-lay language datasets. However, LoRA struggles with the challenges posed by multi-source heterogeneous MLLG datasets. Specifically, through a series of exploratory experiments, we reveal that standard LoRA fail to meet the requirement for semantic fidelity and diverse lay-style generation in MLLG task. To address these limitations, we propose Magical, an asymmetric LoRA architecture tailored for MLLG under heterogeneous data scenarios. Magical employs a shared matrix A for abstractive summarization, along with multiple isolated matrices B for diverse lay-style generation. To preserve semantic fidelity during the lay language generation process, Magical introduces a Semantic Invariance Constraint to mitigate semantic subspace shifts on matrix A. Furthermore, to better adapt to diverse lay-style generation, Magical incorporates the Recommendation-guided Switch, an externally interface to prompt the LLM to switch between different matrices B. Experimental results on three real-world lay language generation datasets demonstrate that Magical consistently outperforms prompt-based methods, vanilla LoRA, and its recent variants, while also reducing trainable parameters by 31. 66%. Our code is publicly available at https: //github. com/tianlwang/Magical. git.

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IROS Conference 2024 Conference Paper

Kinetic-energy-optimal and Safety-guaranteed Trajectory Planning for Bridge Inspection Robot Manipulator

Tianyu Zhang
Yong Chang
Hongguang Wang
Tianlong Wang

Bridge inspections are essential for maintaining key infrastructure and preventing structural and functional failures. Nevertheless, traditional manual inspection techniques are plagued by laboriousness, high risk, and low efficiency. Although numerous automation inspection methods have been studied, inspection performance remains challenging. The main difficulties are redundant mechanisms, complex control, high energy consumption, and limited autonomy and safety. To address these problems, we are developing a small, lightweight, electrically-driven robotic manipulator for bridge inspection named the BIRM. Here, we propose a kinetic-energy-optimal and safety-guaranteed trajectory planning for BIRM. Compared with existing methods, it simultaneously addresses energy consumption and safety. The approach formulates a quadratic programming (QP) problem by considering the robot’s kinetic energy as the objective function, and the augmented Lagrange multiplier (ALM) is applied to find the solution of the QP. The proposed method completely satisfies the joint position, velocity, and acceleration limits at the speed level while considering collision avoidance. In this paper, the collision detection strategy can achieve low-complexity computation through several structural parameters of the bridge, thereby quickly adapting to environmental changes. Through simulation experiments, we validate the effectiveness and superiority of the proposed method. Through physical experiments, we demonstrate the sustainability and safety of bridge inspections in the field.

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