63 citations · 80 across the 15 of their papers we have counts for
18 papers
Sample What You Say: Aligning Language Models to Sample the Distributions They State
Kasra Arabi, Virginia Smith, Chhavi Yadav
Language models are increasingly used to sample from a specified distribution, for instance, to simulate survey respondents or generate synthetic data. Instruction-tuned models can…
Safety Nudges: User-Facing Interventions for Real-Time AI Risk Awareness
Varshini Elangovan, James Wedgwood, Chhavi Yadav +3
Conversational AI systems can pose safety risks to their users such as hallucination, sycophancy, overconfidence, and anthropomorphism, but these risks are difficult for users to d…
Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety
Neil Kale, Rebecca Portnoff, Pratiksha Thaker +5
Modern artificial intelligence (AI) systems present profound new risks to child safety. AI is increasingly being misused to create AI-generated child sexual abuse material, facilit…
Alignment Defends LLMs from Property Inference Attacks
Pengrun Huang, Chhavi Yadav, Ruihan Wu +1
Large language models (LLMs) are increasingly fine-tuned on domain-specific datasets that may contain sensitive, dataset-level properties. Recent work has shown that such dataset-l…
Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks
Kevin Kuo, Chhavi Yadav, Virginia Smith
Recent defenses for safeguarding open-weight large language models (LLMs) are intended to prevent adversarial usage. Underlying these defenses is an assumption that new harmful beh…
Curriculum Learning for Safety Alignment
Sandeep Kumar, Virginia Smith, Chhavi Yadav
Direct Preference Optimisation (DPO) is widely used for safety alignment in large language models. However, prior work shows it is brittle and exhibits poor out-of-distribution (OO…
Automated Concept Discovery for LLM-as-a-Judge Preference Analysis
James Wedgwood, Chhavi Yadav, Virginia Smith
Large Language Models (LLMs) are increasingly used as scalable evaluators of model outputs, but their preference judgments exhibit systematic biases and can diverge from human eval…
Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice
Kevin Kuo, Chhavi Yadav, Virginia Smith
Cross-silo federated learning (FL) is a promising approach to enable cross-organization collaboration in machine learning model development without directly sharing private data. D…
Can We Infer Confidential Properties of Training Data from LLMs?
Pengrun Huang, Chhavi Yadav, Kamalika Chaudhuri +1
Large language models (LLMs) are increasingly fine-tuned on domain-specific datasets to support applications in fields such as healthcare, finance, and law. These fine-tuning datas…
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs
Chhavi Yadav, Evan Monroe Laufer, Dan Boneh +1
In principle, explanations are intended as a way to increase trust in machine learning models and are often obligated by regulations. However, many circumstances where these are de…
Influence-based Attributions can be Manipulated
Chhavi Yadav, Ruihan Wu, Kamalika Chaudhuri
Influence Functions are a standard tool for attributing predictions to training data in a principled manner and are widely used in applications such as data valuation and fairness.…
Evaluating Deep Unlearning in Large Language Models
Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov +1
Machine unlearning has emerged as an important component in developing safe and trustworthy models. Prior work on fact unlearning in LLMs has mostly focused on removing a specified…
FairProof : Confidential and Certifiable Fairness for Neural Networks
Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh +1
Machine learning models are increasingly used in societal applications, yet legal and privacy concerns demand that they very often be kept confidential. Consequently, there is a gr…
Keeping Up with the Language Models: Systematic Benchmark Extension for Bias Auditing
Ioana Baldini, Chhavi Yadav, Manish Nagireddy +2
Bias auditing of language models (LMs) has received considerable attention as LMs are becoming widespread. As such, several benchmarks for bias auditing have been proposed. At the…
XAudit : A Theoretical Look at Auditing with Explanations
Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri
Responsible use of machine learning requires models to be audited for undesirable properties. While a body of work has proposed using explanations for auditing, how to do so and wh…
Behavior of k-NN as an Instance-Based Explanation Method
Chhavi Yadav, Kamalika Chaudhuri
Adoption of DL models in critical areas has led to an escalating demand for sound explanation methods. Instance-based explanation methods are a popular type that return selective i…
On the design of convolutional neural networks for automatic detection of Alzheimer's disease
Sheng Liu, Chhavi Yadav, Carlos Fernandez-Granda +1
Early detection is a crucial goal in the study of Alzheimer's Disease (AD). In this work, we describe several techniques to boost the performance of 3D deep convolutional neural ne…
Cold Case: The Lost MNIST Digits
Chhavi Yadav, Léon Bottou
Although the popular MNIST dataset [LeCun et al., 1994] is derived from the NIST database [Grother and Hanaoka, 1995], the precise processing steps for this derivation have been lo…