Search Results for author: Vaidehi Patil

Found 5 papers, 4 papers with code

Debiasing Multimodal Models via Causal Information Minimization

1 code implementation28 Nov 2023 Vaidehi Patil, Adyasha Maharana, Mohit Bansal

In this paper, we study bias arising from confounders in a causal graph for multimodal data and examine a novel approach that leverages causally-motivated information minimization to learn the confounder representations.

Visual Question Answering (VQA)

Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

1 code implementation29 Sep 2023 Vaidehi Patil, Peter Hase, Mohit Bansal

Experimentally, we show that even state-of-the-art model editing methods such as ROME struggle to truly delete factual information from models like GPT-J, as our whitebox and blackbox attacks can recover "deleted" information from an edited model 38% of the time.

Model Editing

GEMS: Scene Expansion using Generative Models of Graphs

no code implementations8 Jul 2022 Rishi Agarwal, Tirupati Saketh Chandra, Vaidehi Patil, Aniruddha Mahapatra, Kuldeep Kulkarni, Vishwa Vinay

To this end, we formulate scene graph expansion as a sequential prediction task involving multiple steps of first predicting a new node and then predicting the set of relationships between the newly predicted node and previous nodes in the graph.

Graph Generation Image Retrieval +1

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