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Shen Gao

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11 papers

2 author rows

AAAI Conference 2026 Conference Paper

Beyond Superficial Forgetting: Thorough Unlearning Through Knowledge Density Estimation and Block Re-Insertion

Feng Guo
Yuntao Wen
Shen Gao
Junshuo Zhang
Shuo Shang

Machine unlearning, which selectively removes harmful knowledge from a pre-trained model without retraining from scratch, is crucial for addressing privacy, regulatory compliance, and ethical concerns in Large Language Models (LLMs). However, existing unlearning methods often struggle to thoroughly remove harmful knowledge, leaving residual harmful knowledge that can be easily recovered. To address these limitations, we propose Knowledge Density-Guided Unlearning via Blocks Reinsertion (KUnBR), a novel approach that first identifies layers with rich harmful knowledge and then thoroughly eliminates the harmful knowledge via re-insertion strategy. Our method introduces knowledge density estimation to quantify and locate layers containing the most harmful knowledge, enabling precise unlearning. Additionally, we design a layer re-insertion strategy that extracts and re-inserts harmful knowledge-rich layers into the original LLM, bypassing gradient obstruction caused by cover layers and ensuring effective gradient propagation during unlearning. Extensive experiments conducted on several unlearning and general capability benchmarks demonstrate that KUnBR achieves state-of-the-art forgetting performance while maintaining model utility.

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IROS Conference 2025 Conference Paper

An Intelligent Skeleton Based on Liquid Metal for Biohybrid Actuator Powered by Muscle

Xiaoqi Lu
Yuyin Zhang
Yunajie Gan
Shen Gao
Yue Wang
Na Liu
Tao Yue

Biological machines that use biological cells and soft materials in combination to obtain a sense of the environment driven by bioenergy and generate driving force are called biohybrid actuators. With the development of tissue engineering and organoid technology, researchers have applied biohybrid actuators technology to the research of precision medicine and targeted drug delivery, but the research on feedback and evaluation of biohybrid actuation performance is limited to visual and simulation calculations. Therefore, we hope to develop an intelligent crawling skeleton for sensing function, which can be used to evaluate the actuation ability of muscle actuators, and eventually realize the high-precision control of biohybrid actuators. In this work, an intelligent crawling skeleton based on three-dimensional liquid metal is proposed to detect and feedback the crawling of C2C12 muscle actuators. Three-dimensional muscle tissue was composed of mixing hydrogels and cells, and the functionalization of muscle rings was promoted using static mechanical forces and external electric field stimulation. The composite crawling skeleton is fabricated by inverting mold and soft lithography technology. The skeleton can adapt to large deformations above 90 degrees and is more sensitive to deformations by adjusting materials with different elastic modulus. Inspired by the tendon-bone structure, the intelligent crawling skeleton can obtain the deformation degree of the biohybrid actuator in the crawling process according to the characteristics of the deformation from the muscle tissue, and put forward a good idea for the feedback and closed-loop control of the biohybrid actuators.

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IROS Conference 2025 Conference Paper

Self-Sensing Liquid Crystal Elastomer Actuator with Magnetic-Thermal Synergy

Shen Gao
Mingjun Tang
Xiao Lu
Chenghao Zhou
Yuyin Zhang
Tao Yue
Yue Wang

Fueled by the rapid evolution of robotics, the demand for intelligent and lightweight robotic systems continues to grow across industries. However, conventional designs often separate sensing and actuation, resulting in structural complexity and diminished reliability. While integrated sensor-actuator systems offer a promising solution, they face significant challenges in manufacturing and scalability. Liquid crystal elastomer (LCE) are widely utilized in actuators for their thermally responsive deformation and programmability, while Neodymium-Iron-Boron (NdFeB) nanoparticles provide exceptional magnetic properties for sensing. This paper introduces a novel Self-Sensing LCE (SS-LCE) actuator, seamlessly combining LCE and NdFeB to enable simultaneous actuation and self-sensing capabilities. Under thermal stimulation, the actuator executes complex motions while delivering real-time feedback through magnetic field variations. Its programmability and adaptable fabrication process support diverse motion modes, unlocking broad application potential. By enhancing integration, reliability, and flexibility, this self-sensing actuator represents a pivotal advancement in the development of lightweight, intelligent robotic systems with significant research and industrial implications.

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AAAI Conference 2024 Conference Paper

Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum

Shen Gao
Zhengliang Shi
Minghang Zhu
Bowen Fang
Xin Xin
Pengjie Ren
Zhumin Chen
Jun Ma

Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extending the capability of LLMs. Although there are some works that employ open-source LLMs for the tool-learning task, most of them are trained in a controlled environment in which LLMs only learn to execute the human-provided tools. However, selecting proper tools from the large toolset is also a crucial ability for the tool-learning model to be applied in real-world applications. Existing methods usually directly employ self-instruction methods to train the model, which ignores differences in tool complexity. In this paper, we propose the Confucius a novel tool-learning framework to train LLM to use complicated tools in real-world scenarios, which contains two main phases: (1) We first propose a multi-stage learning method to teach the LLM to use various tools from an easy-to-difficult curriculum; (2) thenceforth, we propose the Iterative Self-instruct from Introspective Feedback (ISIF) to dynamically construct the dataset to improve the ability to use the complicated tool. Extensive experiments conducted on both controlled and real-world settings demonstrate the superiority of our tool-learning framework in the real-world application scenario compared to both tuning-free (e.g., ChatGPT, Claude) and tuning-based baselines (e.g., GPT4Tools).

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AAAI Conference 2023 Conference Paper

Contrastive Learning Reduces Hallucination in Conversations

Weiwei Sun
Zhengliang Shi
Shen Gao
Pengjie Ren
Maarten de Rijke
Zhaochun Ren

Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the problem of “hallucination:” they may generate plausible-looking statements that are irrelevant or factually incorrect. To address this problem, we propose a contrastive learning scheme, named MixCL. A novel mixed contrastive objective is proposed to explicitly optimize the implicit knowledge elicitation process of LMs, and thus reduce their hallucination in conversations. We also examine negative sampling strategies of retrieved hard negatives and model-generated negatives. We conduct experiments on Wizard-of-Wikipedia, a public, open-domain knowledge-grounded dialogue benchmark, and assess the effectiveness of MixCL. MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality. We show that MixCL achieves comparable performance to state-of-the-art KB-based approaches while enjoying notable advantages in terms of efficiency and scalability.

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AAAI Conference 2021 Conference Paper

The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement

Mingzhe Li
Xiuying Chen
Min Yang
Shen Gao
Dongyan Zhao
Rui Yan

Eye-catching headlines function as the first device to trigger more clicks, bringing reciprocal effect between producers and viewers. Producers can obtain more traffic and profits, and readers can have access to outstanding articles. When generating attractive headlines, it is important to not only capture the attractive content but also follow an eye-catching written style. In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style. Concretely, we first devise a disentanglement module to divide the style and content of an attractive prototype headline into latent spaces, with two auxiliary constraints to ensure the two spaces are indeed disentangled. The latent content information is then used to further polish the document representation and help capture the salient part. Finally, the generator takes the polished document as input to generate headline under the guidance of the attractive style. Extensive experiments on the public Kuaibao dataset show that DAHG achieves state-ofthe-art performance. Human evaluation also demonstrates that DAHG triggers 22% more clicks than existing models.

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IJCAI Conference 2020 Conference Paper

From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information

Shen Gao
Xiuying Chen
Zhaochun Ren
Dongyan Zhao
Rui Yan

Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words. This research topic has started to attract the attention of a large community of researchers, and it is nowadays counted as one of the most promising research areas. In general, text summarization algorithms aim at using a plain text document as input and then output a summary. However, in real-world applications, most of the data is not in a plain text format. Instead, there is much manifold information to be summarized, such as the summary for a web page based on a query in the search engine, extreme long document (e. g. academic paper), dialog history and so on. In this paper, we focus on the survey of these new summarization tasks and approaches in the real-world application.

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AAAI Conference 2020 Short Paper

RPM-Oriented Query Rewriting Framework for E-commerce Keyword-Based Sponsored Search (Student Abstract)

Xiuying Chen
Daorui Xiao
Shen Gao
Guojun Liu
Wei Lin
Bo Zheng
Dongyan Zhao
Rui Yan

Sponsored search optimizes revenue and relevance, which is estimated by Revenue Per Mille (RPM). Existing sponsored search models are all based on traditional statistical models, which have poor RPM performance when queries follow a heavy-tailed distribution. Here, we propose an RPMoriented Query Rewriting Framework (RQRF) which outputs related bid keywords that can yield high RPM. RQRF embeds both queries and bid keywords to vectors in the same implicit space, converting the rewriting probability between each query and keyword to the distance between the two vectors. For label construction, we propose an RPM-oriented sample construction method, labeling keywords based on whether or not they can lead to high RPM. Extensive experiments are conducted to evaluate performance of RQRF. In a one month large-scale real-world trafﬁc of e-commerce sponsored search system, the proposed model signiﬁcantly outperforms traditional baseline.

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AAAI Conference 2019 Conference Paper

Abstractive Text Summarization by Incorporating Reader Comments

Shen Gao
Xiuying Chen
Piji Li
Zhaochun Ren
Lidong Bing
Dongyan Zhao
Rui Yan

In neural abstractive summarization field, conventional sequence-to-sequence based models often suffer from summarizing the wrong aspect of the document with respect to the main aspect. To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect. Unlike traditional abstractive summarization task, reader-aware summarization confronts two main challenges: (1) Comments are informal and noisy; (2) jointly modeling the news document and the reader comments is challenging. To tackle the above challenges, we design an adversarial learning model named reader-aware summary generator (RASG), which consists of four components: (1) a sequence-to-sequence based summary generator; (2) a reader attention module capturing the reader focused aspects; (3) a supervisor modeling the semantic gap between the generated summary and reader focused aspects; (4) a goal tracker producing the goal for each generation step. The supervisor and the goal tacker are used to guide the training of our framework in an adversarial manner. Extensive experiments are conducted on our large-scale real-world text summarization dataset, and the results show that RASG achieves the stateof-the-art performance in terms of both automatic metrics and human evaluations. The experimental results also demonstrate the effectiveness of each module in our framework. We release our large-scale dataset for further research1.

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IJCAI Conference 2019 Conference Paper

Learning towards Abstractive Timeline Summarization

Xiuying Chen
Zhangming Chan
Shen Gao
Meng-Hsuan Yu
Dongyan Zhao
Rui Yan

Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods. In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, we propose a memory-based timeline summarization model (MTS). Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process. Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events. Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.

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IJCAI Conference 2018 Conference Paper

Get The Point of My Utterance! Learning Towards Effective Responses with Multi-Head Attention Mechanism

Chongyang Tao
Shen Gao
Mingyue Shang
Wei Wu
Dongyan Zhao
Rui Yan

Attention mechanism has become a popular and widely used component in sequence-to-sequence models. However, previous research on neural generative dialogue systems always generates universal responses, and the attention distribution learned by the model always attends to the same semantic aspect. To solve this problem, in this paper, we propose a novel Multi-Head Attention Mechanism (MHAM) for generative dialog systems, which aims at capturing multiple semantic aspects from the user utterance. Further, a regularizer is formulated to force different attention heads to concentrate on certain aspects. The proposed mechanism leads to more informative, diverse, and relevant response generated. Experimental results show that our proposed model outperforms several strong baselines.

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