Search Results for author: Yingchun Wang

Found 17 papers, 7 papers with code

HoneypotNet: Backdoor Attacks Against Model Extraction

no code implementations2 Jan 2025 Yixu Wang, Tianle Gu, Yan Teng, Yingchun Wang, Xingjun Ma

In this work, we introduce a new defense paradigm called attack as defense which modifies the model's output to be poisonous such that any malicious users that attempt to use the output to train a substitute model will be poisoned.

Backdoor Attack model +1

Towards AI-$45^{\circ}$ Law: A Roadmap to Trustworthy AGI

no code implementations8 Dec 2024 Chao Yang, Chaochao Lu, Yingchun Wang, BoWen Zhou

In this position paper, we propose the \textit{AI-\textbf{$45^{\circ}$} Law} as a guiding principle for a balanced roadmap toward trustworthy AGI, and introduce the \textit{Causal Ladder of Trustworthy AGI} as a practical framework.

Decision Making

Reflection-Bench: probing AI intelligence with reflection

1 code implementation21 Oct 2024 Lingyu Li, Yixu Wang, Haiquan Zhao, Shuqi Kong, Yan Teng, Chunbo Li, Yingchun Wang

The ability to adapt beliefs or behaviors in response to unexpected outcomes, reflection, is fundamental to intelligent systems' interaction with the world.

counterfactual Decision Making

MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts

1 code implementation18 Sep 2024 Tianle Gu, Kexin Huang, Ruilin Luo, Yuanqi Yao, Yujiu Yang, Yan Teng, Yingchun Wang

LLM Unlearning, a post-hoc approach to remove this information from trained LLMs, offers a promising solution to mitigate these risks.

Memorization

M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis

1 code implementation24 Jul 2024 Junyu Li, Ye Zhang, Wen Shu, Xiaobing Feng, Yingchun Wang, Pengju Yan, Xiaolin Li, Chulin Sha, Min He

Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysis in computational pathology, enabling a wide range of prediction tasks from tumor subtyping to inferring genetic mutations and multi-omics biomarkers.

Multiple Instance Learning Prediction +1

Building Intelligence Identification System via Large Language Model Watermarking: A Survey and Beyond

no code implementations15 Jul 2024 Xuhong Wang, Haoyu Jiang, Yi Yu, Jingru Yu, Yilun Lin, Ping Yi, Yingchun Wang, Yu Qiao, Li Li, Fei-Yue Wang

Large Language Models (LLMs) are increasingly integrated into diverse industries, posing substantial security risks due to unauthorized replication and misuse.

Language Modeling Language Modelling +1

ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models

3 code implementations21 Jun 2024 Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Wang Jian, Dandan Liang, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang

Inspired by the awesome development of role-playing agents, we propose an ESC Evaluation framework (ESC-Eval), which uses a role-playing agent to interact with ESC models, followed by a manual evaluation of the interactive dialogues.

MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models

1 code implementation11 Jun 2024 Tianle Gu, Zeyang Zhou, Kexin Huang, Dandan Liang, Yixu Wang, Haiquan Zhao, Yuanqi Yao, Xingge Qiao, Keqing Wang, Yujiu Yang, Yan Teng, Yu Qiao, Yingchun Wang

In this paper, we present MLLMGuard, a multidimensional safety evaluation suite for MLLMs, including a bilingual image-text evaluation dataset, inference utilities, and a lightweight evaluator.

Red Teaming

Flames: Benchmarking Value Alignment of LLMs in Chinese

1 code implementation12 Nov 2023 Kexin Huang, Xiangyang Liu, Qianyu Guo, Tianxiang Sun, Jiawei Sun, Yaru Wang, Zeyang Zhou, Yixu Wang, Yan Teng, Xipeng Qiu, Yingchun Wang, Dahua Lin

The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values.

Benchmarking Fairness

Fake Alignment: Are LLMs Really Aligned Well?

1 code implementation10 Nov 2023 Yixu Wang, Yan Teng, Kexin Huang, Chengqi Lyu, Songyang Zhang, Wenwei Zhang, Xingjun Ma, Yu-Gang Jiang, Yu Qiao, Yingchun Wang

The growing awareness of safety concerns in large language models (LLMs) has sparked considerable interest in the evaluation of safety.

Multiple-choice

SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalization

no code implementations19 May 2023 Yingchun Wang, Jingcai Guo, Yi Liu, Song Guo, Weizhan Zhang, Xiangyong Cao, Qinghua Zheng

Based on the idea that in-distribution (ID) data with spurious features may have a lower experience risk, in this paper, we propose a novel Spurious Feature-targeted model Pruning framework, dubbed SFP, to automatically explore invariant substructures without referring to the above drawbacks.

Out-of-Distribution Generalization

Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning

no code implementations9 Feb 2023 Yingchun Wang, Jingcai Guo, Song Guo, Weizhan Zhang

Mixed-precision quantization mostly predetermines the model bit-width settings before actual training due to the non-differential bit-width sampling process, obtaining sub-optimal performance.

Quantization reinforcement-learning +2

Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models

no code implementations9 Feb 2023 Yingchun Wang, Jingcai Guo, Jie Zhang, Song Guo, Weizhan Zhang, Qinghua Zheng

Federated learning (FL) is an emerging technique that trains massive and geographically distributed edge data while maintaining privacy.

Computational Efficiency Fairness +1

Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruning

no code implementations19 Dec 2022 Yingchun Wang, Jingcai Guo, Song Guo, Weizhan Zhang, Jie Zhang

Recent studies show that even highly biased dense networks contain an unbiased substructure that can achieve better out-of-distribution (OOD) generalization than the original model.

Out-of-Distribution Generalization

Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning

no code implementations7 Dec 2022 Yingchun Wang, Song Guo, Jingcai Guo, Weizhan Zhang, Yida Xu, Jie Zhang, Yi Liu

Extensive experiments based on small Cifar-10 and large-scaled ImageNet demonstrate that our method can obtain sparser networks with great generalization performance while providing quantified reliability for the pruned model.

Network Pruning Variational Inference

Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning

no code implementations14 Nov 2022 Yi Liu, Song Guo, Jie Zhang, Qihua Zhou, Yingchun Wang, Xiaohan Zhao

We prove that FedFoA is a model-agnostic training framework and can be easily compatible with state-of-the-art unsupervised FL methods.

Feature Correlation Federated Learning +4

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