Papers by Jaideep Srivastava

5 papers
BiMind: A Dual-Head Reasoning Model with Attention-Geometry Adapter for Incorrect Information Detection (2026.acl-long)

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Challenge: Existing detection approaches struggle to balance content verification with knowledge modification under collapsed attention geometries.
Approach: They propose a dual-head reasoning framework that disentangles content-internal reasoning from knowledge-augmented reasoning.
Outcome: The proposed model outperforms detection approaches and provides interpretable diagnostics on when and why knowledge matters.
The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection (2025.emnlp-main)

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Challenge: a survey examines the interplay between factual accuracy and cognitive biases . misinformation is more than just the existence of incorrect information, it also entails complex relationships between the information and the entities that consume it.
Approach: They examine the interplay between traditional fact-checking and psychological concepts such as cognitive biases, social dynamics, and emotional responses.
Outcome: The findings highlight limitations of current methods and identify opportunities for improvement . they also outline future research directions to create more robust frameworks .
Using Clinical Notes with Time Series Data for ICU Management (D19-1)

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Challenge: Existing work on monitoring patients in ICU has focused on using time series signals from medical instruments.
Approach: They propose to add clinical notes to the time-series data to improve model performance for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting.
Outcome: The proposed model improves on three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting.
SkOTaPA: A Dataset for Skepticism Detection in Online Text after Persuasion Attempt (2024.lrec-main)

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Challenge: Persuasion attempts are a form of persuaded behavior that can be observed in various social settings, such as advertising, public health, political campaigns, and personal relationships.
Approach: They propose to use multiple independent human annotations to detect skepticism in response to persuasion attempts on social media influencer marketing.
Outcome: The proposed corpus detects skepticism in response to persuasion attempts on social media influencer marketing using multiple independent human annotations.
Which Modality should I use - Text, Motif, or Image? : Understanding Graphs with Large Language Models (2024.findings-naacl)

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Challenge: Current research typically employs limited setups with small real-world graphs.
Approach: They propose a new approach to encoding a graph with diverse modalities, such as text, image, and motif, coupled with prompts to approximate a diagram’s global connectivity.
Outcome: The proposed approach improves performance of LLMs in graph structure analysis by focusing on homophily, motif presence, and graph difficulty.

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