Papers by Christophe Dupuy
Coordinated Replay Sample Selection for Continual Federated Learning (2023.emnlp-industry)
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Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard Zemel, Rahul Gupta
| Challenge: | Continual Federated Learning (CFL) combines decentralized learning with continuous learning . ubiquity of personal devices with a network connection offers rich source of data for learning problems . |
| Approach: | They propose to combine decentralized learning with a continuous learning approach . they propose to coordinate gradient-based replay sample selection across clients . |
| Outcome: | The proposed method shows gains early in the low replay size regime, when the budget for storing past data is small. |
FLIRT: Feedback Loop In-context Red Teaming (2024.emnlp-main)
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Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
| Challenge: | Recent work has evaluated the vulnerabilities of large generative models, such as DALL-E, ChatGPT, and GPT-4. |
| Approach: | They propose an automatic red teaming framework that evaluates a given black-box model and exposes its vulnerabilities against unsafe and inappropriate content generation. |
| Outcome: | The proposed framework evaluates a given black-box model and exposes its vulnerabilities against unsafe and inappropriate content generation. |
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks (2022.findings-naacl)
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Bill Yuchen Lin, Chaoyang He, Zihang Ze, Hulin Wang, Yufen Hua, Christophe Dupuy, Rahul Gupta, Mahdi Soltanolkotabi, Xiang Ren, Salman Avestimehr
| Challenge: | Increasing concerns and regulations about data privacy necessitate the study of privacy-preserving, decentralized learning methods for natural language processing tasks. |
| Approach: | They propose a framework for evaluating federated learning methods on four different tasks . they propose federation between Transformer-based language models and FL methods . |
| Outcome: | The proposed framework compares FL methods on four different tasks under non-IID partitioning strategies. |
Canary Extraction in Natural Language Understanding Models (2022.acl-short)
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| Challenge: | Recent literature has focused on Model Inversion Attacks (ModIvA) that can extract training data from model parameters. |
| Approach: | They propose an attack that extracts canaries from NLU training data and reconstructs them using non-sensitive tokens. |
| Outcome: | The proposed attack can reconstruct a four digit code in the training dataset with a probability of 0.5 in its best configuration. |
ADePT: Auto-encoder based Differentially Private Text Transformation (2021.eacl-main)
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| Challenge: | Differential privacy is an important privacy concern when building statistical models on data containing sensitive information. |
| Approach: | They propose a utility-preserving differentially private text transformation algorithm using auto-encoders that can be used to transform text to offer robustness against attacks and produce transformations with high semantic quality. |
| Outcome: | The proposed model performs better against membership inference attacks while offering lower to no degradation in the utility of the underlying transformation process compared to baselines. |
Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning (2023.acl-short)
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Mustafa Ozdayi, Charith Peris, Jack FitzGerald, Christophe Dupuy, Jimit Majmudar, Haidar Khan, Rahil Parikh, Rahul Gupta
| Challenge: | Large Language Models memorize significant portions of training data, which poses privacy risk. |
| Approach: | They propose a prompt-tuning approach to control the extraction rates of memorized content in large language models. |
| Outcome: | The proposed techniques yield 9.3% increase in extraction rate compared to baseline model . the proposed defense achieves 97.7% reduction with a perplexity increase of 16.9% . |