Golnoosh Farnadi

Wasserstein Distributionally Robust Optimization through the Lens of Structural Causal Models and Individual Fairness

Ahmad Reza Ehyaei

Samira Samadi

2024-09-25

NeurIPS.cc/2024/Conference (poster)

Understanding the Local Geometry of Generative Model Manifolds

Ahmed Imtiaz Humayun

Ibtihel Amara

Candice Schumann

Negar Rostamzadeh

Mohammad Havaei

Deep generative models learn continuous representations of complex data manifolds using a finite number of samples during training. For a pr… (voir plus)e-trained generative model, the common way to evaluate the quality of the manifold representation learned, is by computing global metrics like Fr\'echet Inception Distance using a large number of generated and real samples. However, generative model performance is not uniform across the learned manifold, e.g., for \textit{foundation models} like Stable Diffusion generation performance can vary significantly based on the conditioning or initial noise vector being denoised. In this paper we study the relationship between the \textit{local geometry of the learned manifold} and downstream generation. Based on the theory of continuous piecewise-linear (CPWL) generators, we use three geometric descriptors - scaling (

2024-08-15

ArXiv (prépublication)

From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards

Khaoula Chehbouni

Megha Roshan

Emmanuel Ma

Futian Andrew Wei

Afaf Taïk

Jackie Cheung

2024-08-01

Findings of the Association for Computational Linguistics ACL 2024 (publié)

Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild

Niloofar Mireshghallah

Maria Antoniak

Yash More

Yejin Choi

Measuring personal disclosures made in human-chatbot interactions can provide a better understanding of users' AI literacy and facilitate pr… (voir plus)ivacy research for large language models (LLMs). We run an extensive, fine-grained analysis on the personal disclosures made by real users to commercial GPT models, investigating the leakage of personally identifiable and sensitive information. To understand the contexts in which users disclose to chatbots, we develop a taxonomy of tasks and sensitive topics, based on qualitative and quantitative analysis of naturally occurring conversations. We discuss these potential privacy harms and observe that: (1) personally identifiable information (PII) appears in unexpected contexts such as in translation or code editing (48% and 16% of the time, respectively) and (2) PII detection alone is insufficient to capture the sensitive topics that are common in human-chatbot interactions, such as detailed sexual preferences or specific drug use habits. We believe that these high disclosure rates are of significant importance for researchers and data curators, and we call for the design of appropriate nudging mechanisms to help users moderate their interactions.

2024-07-16

ArXiv (prépublication)

Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild

Niloofar Mireshghallah

Maria Antoniak

Yash More

Yejin Choi

Measuring personal disclosures made in human-chatbot interactions can provide a better understanding of users' AI literacy and facilitate pr… (voir plus)ivacy research for large language models (LLMs). We run an extensive, fine-grained analysis on the personal disclosures made by real users to commercial GPT models, investigating the leakage of personally identifiable and sensitive information. To understand the contexts in which users disclose to chatbots, we develop a taxonomy of tasks and sensitive topics, based on qualitative and quantitative analysis of naturally occurring conversations. We discuss these potential privacy harms and observe that: (1) personally identifiable information (PII) appears in unexpected contexts such as in translation or code editing (48% and 16% of the time, respectively) and (2) PII detection alone is insufficient to capture the sensitive topics that are common in human-chatbot interactions, such as detailed sexual preferences or specific drug use habits. We believe that these high disclosure rates are of significant importance for researchers and data curators, and we call for the design of appropriate nudging mechanisms to help users moderate their interactions.

2024-07-16

ArXiv (prépublication)

Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild

Niloofar Mireshghallah

Maria Antoniak

Yash More

Yejin Choi

Measuring personal disclosures made in human-chatbot interactions can provide a better understanding of users' AI literacy and facilitate pr… (voir plus)ivacy research for large language models (LLMs). We run an extensive, fine-grained analysis on the personal disclosures made by real users to commercial GPT models, investigating the leakage of personally identifiable and sensitive information. To understand the contexts in which users disclose to chatbots, we develop a taxonomy of tasks and sensitive topics, based on qualitative and quantitative analysis of naturally occurring conversations. We discuss these potential privacy harms and observe that: (1) personally identifiable information (PII) appears in unexpected contexts such as in translation or code editing (48% and 16% of the time, respectively) and (2) PII detection alone is insufficient to capture the sensitive topics that are common in human-chatbot interactions, such as detailed sexual preferences or specific drug use habits. We believe that these high disclosure rates are of significant importance for researchers and data curators, and we call for the design of appropriate nudging mechanisms to help users moderate their interactions.

2024-07-10

colmweb.org/COLM/2024/Conference (accepté)

Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities

Mohammad Havaei

Negar Rostamzadeh

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

Towards More Realistic Extraction Attacks: An Adversarial Perspective

Yash More

Prakhar Ganesh

2024-07-02

ArXiv (prépublication)

On The Local Geometry of Deep Generative Manifolds

Ahmed Imtiaz Humayun

Ibtihel Amara

Candice Schumann

Negar Rostamzadeh

Mohammad Havaei

In this paper, we study theoretically inspired local geometric descriptors of the data manifolds approximated by pre-trained generative mode… (voir plus)ls. The descriptors – local scaling (ψ), local rank (ν), and local complexity (δ) — characterize the uncertainty, dimensionality, and smoothness on the learned manifold, using only the network weights and architecture. We investigate and emphasize their critical role in understanding generative models. Our analysis reveals that the local geometry is intricately linked to the quality and diversity of generated outputs. Additionally, we see that the geometric properties are distinct for out-of-distribution (OOD) inputs as well as for prompts memorized by Stable Diffusion, showing the possible application of our proposed descriptors for downstream detection and assessment of pre-trained generative models.

2024-06-17

ICML.cc/2024/Workshop/GRaM (publié)

Differentially Private Clustered Federated Learning

Saber Malekmohammadi

Afaf Taïk

2024-05-29

ArXiv (prépublication)

Differentially Private Clustered Federated Learning

Saber Malekmohammadi

Afaf Taïk

Federated learning (FL), which is a decentralized machine learning (ML) approach, often incorporates differential privacy (DP) to provide ri… (voir plus)gorous data privacy guarantees. Previous works attempted to address high structured data heterogeneity in vanilla FL settings through clustering clients (a.k.a clustered FL), but these methods remain sensitive and prone to errors, further exacerbated by the DP noise. This vulnerability makes the previous methods inappropriate for differentially private FL (DPFL) settings with structured data heterogeneity. To address this gap, we propose an algorithm for differentially private clustered FL, which is robust to the DP noise in the system and identifies the underlying clients' clusters correctly. To this end, we propose to cluster clients based on both their model updates and training loss values. Furthermore, for clustering clients' model updates at the end of the first round, our proposed approach addresses the server's uncertainties by employing large batch sizes as well as Gaussian Mixture Models (GMM) to reduce the impact of DP and stochastic noise and avoid potential clustering errors. This idea is efficient especially in privacy-sensitive scenarios with more DP noise. We provide theoretical analysis to justify our approach and evaluate it across diverse data distributions and privacy budgets. Our experimental results show its effectiveness in addressing large structured data heterogeneity in DPFL.

2024-05-29

ArXiv (prépublication)