Vaidehi Patil

Publications and Preprints

• SAFREE: Train-free And Adaptive Guard For Safe Text-to-Image And Video Generation   ICLR 2025
  Jaehong Yoon, Shoubin Yu, Vaidehi Patil, Huaxiu Yao, Mohit Bansal
  Abstract | ArXiv | | Project Webpage

  Recent advances in diffusion models have significantly enhanced their ability to generate high-quality images and videos, but they have also increased the risk of producing unsafe content. Existing unlearning/editing-based methods for safe generation remove harmful concepts from models but face several challenges: They cannot instantly remove harmful concepts without training. Their safe generation capabilities depend on collected training data. They alter model weights, risking degradation in quality for content unrelated to toxic concepts. To address these, we propose SAFREE, a novel, training-free approach for safe T2I and T2V, that does not alter the model's weights. Specifically, we detect a subspace corresponding to a set of toxic concepts in the text embedding space and steer prompt embeddings away from this subspace, thereby filtering out harmful content while preserving intended semantics. To balance the trade-off between filtering toxicity and preserving safe concepts, SAFREE incorporates a novel self-validating filtering mechanism that dynamically adjusts the denoising steps when applying the filtered embeddings. Additionally, we incorporate adaptive re-attention mechanisms within the diffusion latent space to selectively diminish the influence of features related to toxic concepts at the pixel level. In the end, SAFREE ensures coherent safety checking, preserving the fidelity, quality, and safety of the output. SAFREE achieves SOTA performance in suppressing unsafe content in T2I generation compared to training-free baselines and effectively filters targeted concepts while maintaining high-quality images. It also shows competitive results against training-based methods. We extend SAFREE to various T2I backbones and T2V tasks, showcasing its flexibility and generalization. SAFREE provides a robust and adaptable safeguard for ensuring safe visual generation.

• UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning   Preprint
  Vaidehi Patil, Elias Stengel-Eskin, Mohit Bansal
  Abstract | ArXiv |

  User specifications or legal frameworks often require information to be removed from pretrained models, including large language models (LLMs). This requires deleting or "forgetting" a set of data points from an already-trained model, which typically degrades its performance on other data points. Thus, a balance must be struck between removing information and keeping the model's other abilities intact, with a failure to balance this trade-off leading to poor deletion or an unusable model. To this end, we propose UPCORE (Utility-Preserving Coreset Selection), a method-agnostic data selection framework for mitigating collateral damage during unlearning. Finding that the model damage is correlated with the variance of the model's representations on the forget set, we selectively prune the forget set to remove outliers, thereby minimizing model degradation after unlearning. We evaluate UPCORE across three standard unlearning methods consistently achieving a superior balance between the competing objectives of deletion efficacy and model preservation. To better evaluate this trade-off, we introduce a new metric, measuring the area-under-the-curve (AUC) across standard metrics. We find that UPCORE improves both standard metrics and AUC, benefitting from positive transfer between the coreset and pruned points while reducing negative transfer from the forget set to points outside of it.

• Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation   TMLR 2024
  Vaidehi Patil, Yi-Lin Sung, Peter Hase, Jie Peng, Tianlong Chen, Mohit Bansal
  Abstract | ArXiv | | Talk | Slides | Dataset

  Large Language Models (LLMs) trained on massive datasets may inadvertently acquire sensitive information such as personal details and potentially harmful content. This risk is further heightened in multimodal LLMs (aka MLLMs) as they integrate information from multiple modalities (image and text). Adversaries can exploit this stored knowledge by crafting inputs across modalities to extract sensitive details. Evaluating how effectively MLLMs can forget such information (targeted unlearning) necessitates the creation of high-quality, well-annotated image-text pairs. While significant research has addressed the creation of datasets for unlearning within LLMs, it has primarily concentrated on text modality. Creation of analogous datasets for multimodal data and models remain an understudied area. To address this gap, we first introduce a multimodal unlearning benchmark, UnLOK-VQA (Unlearning Outside Knowledge VQA), as well as an “attack and-defense” framework to evaluate methods for deleting specific multimodal knowledge from MLLMs. Our dataset generation process involves an automated pipeline to create samples of varied proximity levels to the target data point for evaluation of generalization and specificity, followed by manual filtering to retain only the high-quality data points. We use this process to extend a visual question-answering dataset for evaluating multimodal information deletion. Next, we present a comprehensive unlearning evaluation involving an attack-and-defense framework consisting of four white box and three blackbox attacks against six unlearning defense objectives. We also design a whitebox attack based on the interpretability of hidden states in LLMs motivated by past work. Our experimental results demonstrate that multimodal extraction attacks (with an attack success rate of 45.5%) are more successful than either image-only (32%) or text-only attacks (39%). The best overall defense mechanism, which removes answer information from internal model hidden states, reduces the success rate of multimodal attack to 15.7%. Furthermore, our findings suggest that larger models exhibit greater resilience to attacks, implying that model scaling could be a valuable strategy for enhancing robustness and developing safer models. UnLOK-VQA thus facilitates a comprehensive evaluation of unlearning in MLLMs and serves as a challenging benchmark for future research in unlearning.

• REFINESUMM: Self-Refining MLLM for Generating a Multimodal Summarization Dataset   ACL 2024
  Vaidehi Patil, Leonardo F. R. Ribeiro, Mengwen Liu, Mohit Bansal, Markus Dreyer
  Abstract | ArXiv | Poster | Slides | Talk |

  Multimodal Large Language Models (MLLMs) excel at synthesizing key information from diverse sources. However, generating accurate and faithful multimodal summaries is challenging, primarily due to the lack of appropriate multimodal datasets for fine-tuning that meaningfully integrate textual and visual modalities. To address this gap, we present a new dataset designed specifically for image-text multimodal summarization, harnessing the capabilities of state-of-the-art MLLMs. We generate summaries from Wikipedia sections and corresponding images and evaluate them across text-based, visual and multimodal dimensions, employing reference-free metrics. To refine the dataset, we: (1) Filter the MLLM-generated summaries by training a critic model on human annotations and using its predictions to remove low-quality summaries; (2) Fine-tune the MLLM with the filtered high-quality summaries; (3) Use the fine-tuned model in turn to regenerate the summaries. This self-refinement process significantly improves summary quality, as measured by human judgements and automatic multimodal metrics, resulting in a valuable dataset for multimodal summarization research. The dataset is publicly available at https://github.com/amazon-science/refinesumm.

• Debiasing Multimodal Models via Causal Information Minimization   EMNLP 2023 Findings
  Vaidehi Patil, Adyasha Maharana, Mohit Bansal
  Abstract | ArXiv | Poster | Slides | Talk |

  Most existing debiasing methods for multimodal models, including causal intervention and inference methods, utilize approximate heuristics to represent the biases, such as shallow features from early stages of training or unimodal features for multimodal tasks like VQA, etc., which may not be accurate. 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. Robust predictive features contain diverse information that helps a model generalize to out-of-distribution data. Hence, minimizing the information content of features obtained from a pretrained biased model helps learn the simplest predictive features that capture the underlying data distribution. We treat these features as confounder representations and use them via methods motivated by causal theory to remove bias from models. We find that the learned confounder representations indeed capture dataset biases, and the proposed debiasing methods improve out-of-distribution (OOD) performance on multiple multimodal datasets without sacrificing in-distribution performance. Additionally, we introduce a novel metric to quantify the sufficiency of spurious features in models’ predictions that further demonstrates the effectiveness of our proposed methods.

• Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks   ICLR 2024 Spotlight
  Vaidehi Patil*, Peter Hase*, Mohit Bansal
  Abstract | ArXiv | Poster | Slides | Talk |

  Applications based on image retrieval require editing and associating in intermediate spaces that are representative of the high-level concepts like objects and their relationships rather than dense, pixel-level representations like RGB images or semantic-label maps. We focus on one such representation, scene graphs, and propose a novel scene expansion task where we enrich an input seed graph by adding new nodes (objects) and the corresponding relationships. To this end, we formulate scene graph expansion as a sequential prediction task involving multiple iterations of first predicting a new node and then predicting the set of relationships between the newly predicted node and previously chosen nodes in the graph. We propose and evaluate a sequencing strategy that retains the clustering patterns amongst nodes. In addition, we leverage external knowledge to train our graph generation model, enabling greater generalization of node predictions. Due to the inefficiency of existing maximum mean discrepancy (MMD) based metrics standard for graph generation problems, we design novel metrics that comprehensively evaluate different aspects of node and relation predictions. We conduct extensive experiments on Visual Genome and VRD datasets to evaluate the expanded scene graphs using the standard MMD based metrics and our proposed metrics. We observe that the graphs generated by our method, GEMS, better represent the real distribution of the scene graphs compared with baseline methods like GraphRNN.

• GEMS: Scene Expansion using Generative Models of Graphs   WACV 2023
  Rishi Agarwal, Tirupati Saketh Chandra, Vaidehi Patil, Aniruddha Mahapatra, Kuldeep Kulkarni, Vishwa Vinay
  Abstract

  Applications based on image retrieval require editing and associating in intermediate spaces that are representative of the high-level concepts like objects and their relationships rather than dense, pixellevel representations like RGB images or semantic-label maps. We focus on one such representation, scene graphs, and propose a novel scene expansion task where we enrich an input seed graph by adding new nodes (objects) and the corresponding relationships. 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. We propose a sequencing strategy for observed graphs that retains the clustering patterns amongst nodes. In addition, we leverage external knowledge to train our graph generation model, enabling greater generalization of node predictions. Due to the inefficiency of existing maximum mean discrepancy (MMD) based metrics for graph generation problems in evaluating predicted relationships between nodes (objects), we design novel metrics that comprehensively evaluate different aspects of predicted relations. We conduct extensive experiments on Visual Genome and VRD datasets to evaluate the expanded scene graphs using the standard MMD based metrics and our proposed metrics. We observe that the graphs generated by our method, GEMS, better represent the real distribution of the scene graphs than the baseline methods like GraphRNN.

• Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages   ACL 2022 Oral
  Vaidehi Patil, Partha Talukdar, Sunita Sarawagi
  Abstract | ArXiv | Poster | Slides | Talk |

  Pre-trained multilingual language models such as mBERT and XLM-R have demonstrated great potential for zero-shot cross-lingual transfer to low web-resource languages (LRL). However, due to limited model capacity, the large difference in the sizes of available monolingual corpora between high web-resource languages (HRL) and LRLs does not provide enough scope of co-embedding the LRL with the HRL, thereby affecting downstream task performance of LRLs. In this paper, we argue that relatedness among languages in a language family along the dimension of lexical overlap may be leveraged to overcome some of the corpora limitations of LRLs. We propose Overlap BPE (OBPE), a simple yet effective modification to the BPE vocabulary generation algorithm which enhances overlap across related languages. Through extensive experiments on multiple NLP tasks and datasets, we observe that OBPE generates a vocabulary that increases the representation of LRLs via tokens shared with HRLs. This results in improved zero-shot transfer from related HRLs to LRLs without reducing HRL representation and accuracy. Unlike previous studies that dismissed the importance of token-overlap, we show that in the low-resource related language setting, token overlap matters. Synthetically reducing the overlap to zero can cause as much as a four-fold drop in zero-shot transfer accuracy.

• Detecting Document Versions and Their Ordering In a Collection   WISE 2021 (Best Paper Runner-Up)
  Natwar Modani, Anurag Maurya, Gaurav Verma, Inderjeet Nair, Vaidehi Patil, Anirudh Kanfade
  Abstract | ArXiv |

  Given the iterative and collaborative nature of authoring and the need to adapt the documents for different audience, people end up with a large number of versions of their documents. These additional versions of documents increase the required cognitive effort for various tasks for humans (such as finding the latest version of a document, or organizing documents), and may degrade the performance of machine tasks such as clustering or recommendation of documents. To the best of our knowledge, the task of identifying and ordering the versions of documents from a collection of documents has not been addressed in prior literature. We propose a three-stage approach for the task of identifying versions and ordering them correctly in this paper. We also create a novel dataset for this purpose from Wikipedia, which we are releasing to the research community (https://github.com/natwar-modani/versions). We show that our proposed approach significantly outperforms state-of-the-art approach adapted for this task from the closest previously known task of Near Duplicate Detection, which justifies defining this problem as a novel challenge.

• Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study   ACL 2021
  Yash Khemchandani, Sarvesh Mehtani, Vaidehi Patil, Abhijeet Awasthi, Partha Talukdar, Sunita Sarawagi
  Abstract | ArXiv | Poster | Slides | Talk |

  Recent research in multilingual language models (LM) has demonstrated their ability to effectively handle multiple languages in a single model. This holds promise for low web-resource languages (LRL) as multilingual models can enable transfer of supervision from high resource languages to LRLs. However, incorporating a new language in an LM still remains a challenge, particularly for languages with limited corpora and in unseen scripts. In this paper we argue that relatedness among languages in a language family may be exploited to overcome some of the corpora limitations of LRLs, and propose RelateLM. We focus on Indian languages, and exploit relatedness along two dimensions (1) script (since many Indic scripts originated from the Brahmic script), and (2) sentence structure. RelateLM uses transliteration to convert the unseen script of limited LRL text into the script of a Related Prominent Language (RPL) (Hindi in our case). While exploiting similar sentence structures, RelateLM utilizes readily available bilingual dictionaries to pseudo translate RPL text into LRL corpora. Experiments on multiple real-world benchmark datasets provide validation to our hypothesis that using a related language as pivot, along with transliteration and pseudo translation based data augmentation, can be an effective way to adapt LMs for LRLs, rather than direct training or pivoting through English.