Kerem Zaman — accepted AI-conference papers

2026

DiffVax: Optimization-Free Image Immunization Against Diffusion-Based Editing

Tarik Can Ozden, Ozgur Kara, Oguzhan Akcin, Kerem Zaman, Shashank Srivastava, Sandeep P. Chinchali +1

ICLR 2026poster

Current image immunization defense techniques against diffusion-based editing embed imperceptible noise into target images to disrupt editing models. However, these methods face scalability challenges, as they require time-consuming optimization for each image separately, taking hours for small batc…

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2025

A Causal Lens for Evaluating Faithfulness Metrics

Kerem Zaman, Shashank Srivastava

EMNLP 2025

Large Language Models (LLMs) offer natural language explanations as an alternative to feature attribution methods for model interpretability. However, despite their plausibility, they may not reflect the model’s truereasoning faithfully. While several faithfulness metrics have been proposed, they ar

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2025

INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models

Aum Kendapadi, Kerem Zaman, Rakesh R Menon, Shashank Srivastava

ACL 2025long

Large language models (LLMs) excel at answering questions but remain passive learners—absorbing static data without the ability to question and refine knowledge. This paper explores how LLMs can transition to interactive, question-driven learning through student-teacher dialogues. We introduce INTER…

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2024

Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion

Kerem Zaman, Leshem Choshen, Shashank Srivastava

EMNLP 2024main

Model fusion research aims to aggregate the knowledge of multiple individual models to enhance performance by combining their weights. In this work, we study the inverse problem: investigating whether model fusion can be used to reduce unwanted knowledge. We investigate the effects of model fusion i…

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2023

MaNtLE: Model-agnostic Natural Language Explainer

Rakesh R Menon, Kerem Zaman, Shashank Srivastava

EMNLP 2023long main

Understanding the internal reasoning behind the predictions of machine learning systems is increasingly vital, given their rising adoption and acceptance. While previous approaches, such as LIME generate algorithmic explanations by attributing importance to input features for individual examples, re…

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2022

A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference

Kerem Zaman, Yonatan Belinkov

EMNLP 2022main

Most evaluations of attribution methods focus on the English language. In this work, we present a multilingual approach for evaluating attribution methods for the Natural Language Inference (NLI) task in terms of faithfulness and plausibility.First, we introduce a novel cross-lingual strategy to mea…

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