Papers by Sandeep Polisetty
MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration (2025.acl-long)
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| Challenge: | Existing methods calibrate model confidence on entire response, which leads to incorrect answers with high confidence. |
| Approach: | They propose a framework that advances the knowledge boundary awareness of multimodal large language models through reasoning step confidence calibration. |
| Outcome: | Empirical results show that the proposed framework outperforms existing methods across domains and metrics. |
Unveiling the Lack of LVLM Robustness to Fundamental Visual Variations: Why and Path Forward (2025.findings-acl)
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| Challenge: | Large Vision Language Models (LVLMs) have shown impressive performance on various vision-language tasks. |
| Approach: | They propose a benchmark framework for evaluating Visual Variation Robustness of Large Vision Language Models that incorporates automated evaluation dataset generation and principled metrics for thorough robustness assessment. |
| Outcome: | The proposed framework identifies a vulnerability to visual variations affecting even advanced models that excel at complex vision-language tasks but significantly underperform on simple tasks like object recognition. |
MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness (2025.emnlp-main)
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| Challenge: | Large language models produce non-existing facts when faced with questions outside their parametric knowledge, which undermines their reliability. |
| Approach: | They propose a method that separates the learning of answer prediction and confidence estimation during fine-tuning on instruction data. |
| Outcome: | Experiments on multiple models and different model sizes show that the proposed method outperforms baselines by up to 25% in average precision. |
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection (2021.acl-long)
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Yi Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen McKeown, Mohit Bansal, Avi Sil
| Challenge: | a novel approach to detect fake news is needed due to training data scarcity . current methods focus on document-level fake news detection using lexical features and semantic embeddings . |
| Approach: | They propose a novel benchmark for fake news detection at the knowledge element level . they propose synthesis method which manipulates knowledge elements to generate noisy training data . |
| Outcome: | The proposed method outperforms the state-of-the-art in detecting misinformation . it yields fine-grained explanations and outperformed the current methods . |
TAMP: Token-Adaptive Layerwise Pruning in Multimodal Large Language Models (2025.findings-acl)
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| Challenge: | Existing pruning methods fail to account for unique token attributes across layers and modalities inherent to MLLMs. |
| Approach: | They propose a pruning framework that takes into account unique token attributes across layers and modalities inherent to MLLMs. |
| Outcome: | The proposed pruning framework outperforms existing pruning techniques on two state-of-the-art MLLMs. |