Papers by Mehrab Tanjim
Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes (2025.naacl-short)
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Isabel O. Gallegos, Ryan Aponte, Ryan A. Rossi, Joe Barrow, Mehrab Tanjim, Tong Yu, Hanieh Deilamsalehy, Ruiyi Zhang, Sungchul Kim, Franck Dernoncourt, Nedim Lipka, Deonna Owens, Jiuxiang Gu
| Challenge: | Large language models exhibit harmful social biases, but they are often difficult to train and modify. |
| Approach: | They leverage the zero-shot capabilities of large language models to reduce stereotyping . they introduce a technique called zero- shot self-debiasing to reduce bias . |
| Outcome: | The proposed technique reduces stereotyping across nine different social groups while relying on the LLM itself and a simple prompt. |
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models (2025.findings-emnlp)
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Yeonjun In, Wonjoong Kim, Kanghoon Yoon, Sungchul Kim, Mehrab Tanjim, Sangwu Park, Kibum Kim, Chanyoung Park
| Challenge: | Extensive benchmarks evaluate LLM safety relying heavily on general standards . no benchmark datasets exist to evaluate the user-specific safety of LLMs . |
| Approach: | a new benchmark is designed to assess user-specific aspect of LLM safety . authors propose a simple remedy based on chain-of-thought to improve user-specified safety. |
| Outcome: | a new benchmark assesses the user-specific aspect of LLM safety . the proposed solution improves user-specified safety by chain-of-thought . |
DELOC: Document Element Localizer (2025.emnlp-main)
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| Challenge: | Existing methods to edit PDFs using natural language are ineffective at grounding the edit location effectively. |
| Approach: | They propose a system to ground PDF edit request spatially using a model to predict the edit location in the PDF. |
| Outcome: | The proposed system outperforms existing Multimodal Large Language Models and specialized models on DocEdit. |
Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey (2025.emnlp-main)
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Mehrab Tanjim, Yeonjun In, Xiang Chen, Victor Bursztyn, Ryan A. Rossi, Sungchul Kim, Guang-Jie Ren, Vaishnavi Muppala, Shun Jiang, Yongsung Kim, Chanyoung Park
| Challenge: | Existing literature on ambiguity and disambiguation with Large Language Models (LLMs) ambiguities are a fundamental challenge in human-AI interactions due to complexity and flexibility of human language. |
| Approach: | They propose to define key terms and concepts and categorize various disambiguation approaches enabled by LLMs and provide a comparative analysis of their advantages and disadvantages. |
| Outcome: | The proposed frameworks are compared against different disambiguation approaches and highlight their relevance for future research. |
GUI Agents: A Survey (2025.findings-acl)
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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt
| Challenge: | Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life. |
| Approach: | They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities. |
| Outcome: | The proposed framework delineates their perception, reasoning, planning, and acting capabilities. |
VISIAR: Empower MLLM for Visual Story Ideation (2025.findings-acl)
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Zhaoyang Xia, Somdeb Sarkhel, Mehrab Tanjim, Stefano Petrangeli, Ishita Dasgupta, Yuxiao Chen, Jinxuan Xu, Di Liu, Saayan Mitra, Dimitris N. Metaxas
| Challenge: | Existing literature on visual storytelling has not explored the ideation process fully. |
| Approach: | They propose a visual story ideation task that automates the selection and arrangement of visual assets into coherent sequences that convey expressive storylines. |
| Outcome: | The proposed framework surpasses baseline by 33.5% and 18.5%, respectively, on three metrics. |
Identity-Robust Language Model Generation via Content Integrity Preservation (2026.acl-long)
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| Challenge: | Existing studies show that Large Language Model outputs vary across sociodemographic attributes . this causes disparities in factual accuracy, utility, and safety, even for objective questions . |
| Approach: | They propose a lightweight framework for identity-robust generation that neutralizes non-critical identity information while preserving semantically essential attributes. |
| Outcome: | The proposed framework reduces identity-dependent generation bias by 66.3% over vanilla prompting and outperforms existing prompt-based defenses. |
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering (2025.naacl-long)
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| Challenge: | Existing approaches to address ambiguous questions are limited in their efficiency and performance. |
| Approach: | They propose a retrieval augmented generation framework that diversifies and verifies the retrieved passages to encompass diverse interpretations and adapts the most suitable approach tailored to their quality. |
| Outcome: | The proposed approach improves accuracy and robustness by handling low quality retrieval issue in ambiguous questions while enhancing efficiency. |