Search Results for author: Boxin Wang

Found 24 papers, 18 papers with code

Identifying and Mitigating Vulnerabilities in LLM-Integrated Applications

no code implementations7 Nov 2023 Fengqing Jiang, Zhangchen Xu, Luyao Niu, Boxin Wang, Jinyuan Jia, Bo Li, Radha Poovendran

Successful exploits of the identified vulnerabilities result in the users receiving responses tailored to the intent of a threat initiator.

Code Completion

InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining

1 code implementation11 Oct 2023 Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, Bryan Catanzaro

After instruction tuning on Retro, InstructRetro demonstrates significant improvement over the instruction tuned GPT on a wide range of zero-shot tasks.

Decoder Question Answering +3

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

no code implementations NeurIPS 2023 Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li

Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications such as healthcare and finance -- where mistakes can be costly.

Adversarial Robustness Ethics +1

FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data

no code implementations21 Jul 2022 Wenda Chu, Chulin Xie, Boxin Wang, Linyi Li, Lang Yin, Arash Nourian, Han Zhao, Bo Li

However, due to the heterogeneous nature of local data, it is challenging to optimize or even define fairness of the trained global model for the agents.

Fairness Federated Learning

SemAttack: Natural Textual Attacks via Different Semantic Spaces

1 code implementation Findings (NAACL) 2022 Boxin Wang, Chejian Xu, Xiangyu Liu, Yu Cheng, Bo Li

In particular, SemAttack optimizes the generated perturbations constrained on generic semantic spaces, including typo space, knowledge space (e. g., WordNet), contextualized semantic space (e. g., the embedding space of BERT clusterings), or the combination of these spaces.

Adversarial Text

Certifying Out-of-Domain Generalization for Blackbox Functions

1 code implementation3 Feb 2022 Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang

As a result, the wider application of these techniques is currently limited by its scalability and flexibility -- these techniques often do not scale to large-scale datasets with modern deep neural networks or cannot handle loss functions which may be non-smooth such as the 0-1 loss.

Domain Generalization

Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models

1 code implementation4 Nov 2021 Boxin Wang, Chejian Xu, Shuohang Wang, Zhe Gan, Yu Cheng, Jianfeng Gao, Ahmed Hassan Awadallah, Bo Li

In this paper, we present Adversarial GLUE (AdvGLUE), a new multi-task benchmark to quantitatively and thoroughly explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks.

Adversarial Attack Adversarial Robustness +1

DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation

2 code implementations20 Mar 2021 Boxin Wang, Fan Wu, Yunhui Long, Luka Rimanic, Ce Zhang, Bo Li

In this paper, we aim to explore the power of generative models and gradient sparsity, and propose a scalable privacy-preserving generative model DATALENS.

Dimensionality Reduction Navigate +1

Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability

2 code implementations25 Jun 2020 Kaizhao Liang, Jacky Y. Zhang, Boxin Wang, Zhuolin Yang, Oluwasanmi Koyejo, Bo Li

Knowledge transferability, or transfer learning, has been widely adopted to allow a pre-trained model in the source domain to be effectively adapted to downstream tasks in the target domain.

Transfer Learning

Towards Evaluating the Robustness of Chinese BERT Classifiers

no code implementations7 Apr 2020 Boxin Wang, Boyuan Pan, Xin Li, Bo Li

Recent advances in large-scale language representation models such as BERT have improved the state-of-the-art performances in many NLP tasks.

Improving Certified Robustness via Statistical Learning with Logical Reasoning

1 code implementation28 Feb 2020 Zhuolin Yang, Zhikuan Zhao, Boxin Wang, Jiawei Zhang, Linyi Li, Hengzhi Pei, Bojan Karlas, Ji Liu, Heng Guo, Ce Zhang, Bo Li

Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently.

BIG-bench Machine Learning Logical Reasoning

Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States

1 code implementation9 Feb 2020 Yunan Ye, Hengzhi Pei, Boxin Wang, Pin-Yu Chen, Yada Zhu, Jun Xiao, Bo Li

Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e. g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.

Management reinforcement-learning +1

T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack

3 code implementations EMNLP 2020 Boxin Wang, Hengzhi Pei, Boyuan Pan, Qian Chen, Shuohang Wang, Bo Li

In particular, we propose a tree-based autoencoder to embed the discrete text data into a continuous representation space, upon which we optimize the adversarial perturbation.

Adversarial Text Decoder +4

AdvCodec: Towards A Unified Framework for Adversarial Text Generation

no code implementations25 Sep 2019 Boxin Wang, Hengzhi Pei, Han Liu, Bo Li

In particular, we propose a tree based autoencoder to encode discrete text data into continuous vector space, upon which we optimize the adversarial perturbation.

Adversarial Text Question Answering +3

Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms

3 code implementations22 Aug 2019 Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nezihe Merve Gurel, Bo Li, Ce Zhang, Costas J. Spanos, Dawn Song

The most surprising result is that for unweighted $K$NN classifiers and regressors, the Shapley value of all $N$ data points can be computed, exactly, in $O(N\log N)$ time -- an exponential improvement on computational complexity!

Data Valuation Fairness

G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators

2 code implementations NeurIPS 2021 Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl A. Gunter, Bo Li

In particular, we train a student data generator with an ensemble of teacher discriminators and propose a novel private gradient aggregation mechanism to ensure differential privacy on all information that flows from teacher discriminators to the student generator.

BIG-bench Machine Learning Privacy Preserving

Towards Efficient Data Valuation Based on the Shapley Value

1 code implementation27 Feb 2019 Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gurel, Bo Li, Ce Zhang, Dawn Song, Costas Spanos

In this paper, we study the problem of data valuation by utilizing the Shapley value, a popular notion of value which originated in cooperative game theory.

Data Valuation

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