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Vaidehi Patil

7 accepted papers

2026

Generalized Correctness Models: Learning Calibrated and Cross-Model Correctness Predictors from Historical Patterns

ICML 2026poster

Generating accurate and calibrated confidence estimates is critical for deploying LLMs in high-stakes or user-facing applications, and remains an open challenge. Prior research has often framed confidence as a problem of eliciting a model’s “self-knowledge”, i.e., the ability of an LLM to judge whet…

Cited by 0SourceScholar
2025

SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generation

ICLR 2025poster

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 seve…

Cited by 20SourcePDFScholar
2024

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

ICLR 2024spotlight

Pretrained language models sometimes possess knowledge that we do not wish them to, including memorized personal information and knowledge that could be used to harm people. They can also output toxic or harmful text. To mitigate these safety and informational issues, we propose an attack-and-defens…

2024

REFINESUMM: Self-Refining MLLM for Generating a Multimodal Summarization Dataset

ACL 2024long

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 vis…

2023

Debiasing Multimodal Models via Causal Information Minimization

EMNLP 2023long findings

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 accura…

Cited by 0SourcecodeScholar
2022

Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages

ACL 2022long

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-res…

2021

Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study

ACL 2021long

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. Howe…