29 citations · 66 across the 14 of their papers we have counts for
15 papers
Code Monitor Red Teaming for Public-Test-Passing Code
Junchi Liao, Jiawen Deng, Fuji Ren
Visible tests are a common gate for LLM-generated code, but passing them does not certify specification correctness. We study a deployment-like monitoring problem: after code has p…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
Coordinated Replay Sample Selection for Continual Federated Learning
Jack Good, Jimit Majmudar, Christophe Dupuy +5
Continual Federated Learning (CFL) combines Federated Learning (FL), the decentralized learning of a central model on a number of client devices that may not communicate their data…
FLIRT: Feedback Loop In-context Red Teaming
Ninareh Mehrabi, Palash Goyal, Christophe Dupuy +6
Warning: this paper contains content that may be inappropriate or offensive. As generative models become available for public use in various applications, testing and analyzing vul…
Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning
Mustafa Safa Ozdayi, Charith Peris, Jack FitzGerald +5
Large Language Models (LLMs) are known to memorize significant portions of their training data. Parts of this memorized content have been shown to be extractable by simply querying…
Differentially Private Decoding in Large Language Models
Jimit Majmudar, Christophe Dupuy, Charith Peris +3
Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart. In practice, the pre-traine…
Canary Extraction in Natural Language Understanding Models
Rahil Parikh, Christophe Dupuy, Rahul Gupta
Natural Language Understanding (NLU) models can be trained on sensitive information such as phone numbers, zip-codes etc. Recent literature has focused on Model Inversion Attacks (…
Learnings from Federated Learning in the Real world
Christophe Dupuy, Tanya G. Roosta, Leo Long +3
Federated Learning (FL) applied to real world data may suffer from several idiosyncrasies. One such idiosyncrasy is the data distribution across devices. Data across devices could…
An Efficient DP-SGD Mechanism for Large Scale NLP Models
Christophe Dupuy, Radhika Arava, Rahul Gupta +1
Recent advances in deep learning have drastically improved performance on many Natural Language Understanding (NLU) tasks. However, the data used to train NLU models may contain pr…
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks
Bill Yuchen Lin, Chaoyang He, Zihang Zeng +7
Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP…
ADePT: Auto-encoder based Differentially Private Text Transformation
Satyapriya Krishna, Rahul Gupta, Christophe Dupuy
Privacy is an important concern when building statistical models on data containing personal information. Differential privacy offers a strong definition of privacy and can be used…
Self-Attention Gazetteer Embeddings for Named-Entity Recognition
Stanislav Peshterliev, Christophe Dupuy, Imre Kiss
Recent attempts to ingest external knowledge into neural models for named-entity recognition (NER) have exhibited mixed results. In this work, we present GazSelfAttn, a novel gazet…
Learning Determinantal Point Processes in Sublinear Time
Christophe Dupuy, Francis Bach
We propose a new class of determinantal point processes (DPPs) which can be manipulated for inference and parameter learning in potentially sublinear time in the number of items. T…
Decentralized Topic Modelling with Latent Dirichlet Allocation
Igor Colin, Christophe Dupuy
Privacy preserving networks can be modelled as decentralized networks (e.g., sensors, connected objects, smartphones), where communication between nodes of the network is not contr…
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling
Christophe Dupuy, Francis Bach
We study parameter inference in large-scale latent variable models. We first propose an unified treatment of online inference for latent variable models from a non-canonical expone…