172 citations · 446 across the 27 of their papers we have counts for
7 papers · 1 filter
Certifying LLM Safety against Adversarial Prompting
Aounon Kumar, Chirag Agarwal, Suraj Srinivas +3
Large language models (LLMs) are vulnerable to adversarial attacks that add malicious tokens to an input prompt to bypass the safety guardrails of an LLM and cause it to produce ha…
Discriminative Feature Attributions: Bridging Post Hoc Explainability and Inherent Interpretability
Usha Bhalla, Suraj Srinivas, Himabindu Lakkaraju
With the increased deployment of machine learning models in various real-world applications, researchers and practitioners alike have emphasized the need for explanations of model…
Characterizing Data Point Vulnerability via Average-Case Robustness
Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
Studying the robustness of machine learning models is important to ensure consistent model behaviour across real-world settings. To this end, adversarial robustness is a standard f…
Consistent Explanations in the Face of Model Indeterminacy via Ensembling
Dan Ley, Leonard Tang, Matthew Nazari +3
This work addresses the challenge of providing consistent explanations for predictive models in the presence of model indeterminacy, which arises due to the existence of multiple (…
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
Anna P. Meyer, Dan Ley, Suraj Srinivas +1
The Right to Explanation is an important regulatory principle that allows individuals to request actionable explanations for algorithmic decisions. However, several technical chall…
Word-Level Explanations for Analyzing Bias in Text-to-Image Models
Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru +2
Text-to-image models take a sentence (i.e., prompt) and generate images associated with this input prompt. These models have created award wining-art, videos, and even synthetic da…