59 citations · 70 across the 17 of their papers we have counts for
20 papers
Rethinking Failure Attribution in Multi-Agent Systems: A Multi-Perspective Benchmark and Evaluation
Yeonjun In, Mehrab Tanjim, Jayakumar Subramanian +6
Failure attribution is essential for diagnosing and improving multi-agent systems (MAS), yet existing benchmarks and methods largely assume a single deterministic root cause for ea…
GT-SVJ: Generative-Transformer-Based Self-Supervised Video Judge For Efficient Video Reward Modeling
Shivanshu Shekhar, Uttaran Bhattacharya, Raghavendra Addanki +3
Aligning video generative models with human preferences remains challenging: current approaches rely on Vision-Language Models (VLMs) for reward modeling, but these models struggle…
Identity-Robust Language Model Generation via Content Integrity Preservation
Miao Zhang, Kelly Chen, Md Mehrab Tanjim +1
Large Language Model (LLM) outputs often vary across user sociodemographic attributes, leading to disparities in factual accuracy, utility, and safety, even for objective questions…
Calibrating MLLM-as-a-judge via Multimodal Bayesian Prompt Ensembles
Eric Slyman, Mehrab Tanjim, Kushal Kafle +1
Multimodal large language models (MLLMs) are increasingly used to evaluate text-to-image (TTI) generation systems, providing automated judgments based on visual and textual context…
Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey
Md Mehrab Tanjim, Yeonjun In, Xiang Chen +8
Ambiguity remains a fundamental challenge in Natural Language Processing (NLP) due to the inherent complexity and flexibility of human language. With the advent of Large Language M…
ECLAIR: Enhanced Clarification for Interactive Responses in an Enterprise AI Assistant
John Murzaku, Zifan Liu, Vaishnavi Muppala +3
Large language models (LLMs) have shown remarkable progress in understanding and generating natural language across various applications. However, they often struggle with resolvin…
ECLAIR: Enhanced Clarification for Interactive Responses
John Murzaku, Zifan Liu, Md Mehrab Tanjim +3
We present ECLAIR (Enhanced CLArification for Interactive Responses), a novel unified and end-to-end framework for interactive disambiguation in enterprise AI assistants. ECLAIR ge…
SKALD: Learning-Based Shot Assembly for Coherent Multi-Shot Video Creation
Chen Yi Lu, Md Mehrab Tanjim, Ishita Dasgupta +4
We present SKALD, a multi-shot video assembly method that constructs coherent video sequences from candidate shots with minimal reliance on text. Central to our approach is the Lea…
Exploring Rewriting Approaches for Different Conversational Tasks
Md Mehrab Tanjim, Ryan A. Rossi, Mike Rimer +9
Conversational assistants often require a question rewriting algorithm that leverages a subset of past interactions to provide a more meaningful (accurate) answer to the user's que…
Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
Bo Ni, Zheyuan Liu, Leyao Wang +17
Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retriev…
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models
Yeonjun In, Wonjoong Kim, Kanghoon Yoon +5
As the use of large language model (LLM) agents continues to grow, their safety vulnerabilities have become increasingly evident. Extensive benchmarks evaluate various aspects of L…
Detecting Ambiguities to Guide Query Rewrite for Robust Conversations in Enterprise AI Assistants
Md Mehrab Tanjim, Xiang Chen, Victor S. Bursztyn +8
Multi-turn conversations with an Enterprise AI Assistant can be challenging due to conversational dependencies in questions, leading to ambiguities and errors. To address this, we…
GUI Agents: A Survey
Dang Nguyen, Jian Chen, Yu Wang +27
Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomo…
Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering
Yeonjun In, Sungchul Kim, Ryan A. Rossi +4
The retrieval augmented generation (RAG) framework addresses an ambiguity in user queries in QA systems by retrieving passages that cover all plausible interpretations and generati…
X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation
Hanjia Lyu, Ryan Rossi, Xiang Chen +4
Large Language Models (LLMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems. However, most ex…
Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow +6
Large language models (LLMs) have shown remarkable advances in language generation and understanding but are also prone to exhibiting harmful social biases. While recognition of th…
Bias and Fairness in Large Language Models: A Survey
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow +6
Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touc…
VADER: Video Alignment Differencing and Retrieval
Alexander Black, Simon Jenni, Tu Bui +5
We propose VADER, a spatio-temporal matching, alignment, and change summarization method to help fight misinformation spread via manipulated videos. VADER matches and coarsely alig…
Generating Rationales in Visual Question Answering
Hammad A. Ayyubi, Md. Mehrab Tanjim, Julian J. McAuley +1
Despite recent advances in Visual QuestionAnswering (VQA), it remains a challenge todetermine how much success can be attributedto sound reasoning and comprehension ability.We seek…
Enforcing Reasoning in Visual Commonsense Reasoning
Hammad A. Ayyubi, Md. Mehrab Tanjim, David J. Kriegman
The task of Visual Commonsense Reasoning is extremely challenging in the sense that the model has to not only be able to answer a question given an image, but also be able to learn…