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Mukesh Prasad

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

AAAI Conference 2026 Short Paper

When Equal Isn’t Fair: Mitigating Over-Normalization in Large Language Models (Student Abstract)

Ravada Satyadev
Aditya Ganesh Kumar
Avinash Anand
Rajiv Ratn Shah
Zhengkui Wang
Mukesh Prasad

Bias in Large Language Models (LLMs) is increasingly addressed through fairness-oriented techniques. However, in some cases, these approaches may inadvertently remove genuine cultural differences between groups, leading to “over-normalization” or models losing important socio-cultural distinctions. In this work, we introduce OverNormEval, a benchmark designed to detect when an LLM exhibits such over-normalization. We further explore the use of Direct Preference Optimization (DPO) to mitigate over-normalization.

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AAAI Conference 2024 Short Paper

DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)

Aryaman Rao
Parth Singh
Dinesh Kumar Vishwakarma
Mukesh Prasad

Influence Maximization is the task of selecting optimal nodes maximising the influence spread in social networks. This study proposes a Discretized Quantum-based Salp Swarm Algorithm (DQSSA) for optimizing influence diffusion in social networks. By discretizing meta-heuristic algorithms and infusing them with quantum-inspired enhancements, we address issues like premature convergence and low efficacy. The proposed method, guided by quantum principles, offers a promising solution for Influence Maximisation. Experiments on four real-world datasets reveal DQSSA's superior performance as compared to established cutting-edge algorithms.

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AAAI Conference 2023 Short Paper

An Emotion-Guided Approach to Domain Adaptive Fake News Detection Using Adversarial Learning (Student Abstract)

Arkajyoti Chakraborty
Inder Khatri
Arjun Choudhry
Pankaj Gupta
Dinesh Kumar Vishwakarma
Mukesh Prasad

Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance. However, the cross-domain impact of emotion-guided features for fake news detection still remains an open problem. In this work, we propose an emotion-guided, domain-adaptive, multi-task approach for cross-domain fake news detection, proving the efficacy of emotion-guided models in cross-domain settings for various datasets.

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AAAI Conference 2023 Short Paper

CKS: A Community-Based K-shell Decomposition Approach Using Community Bridge Nodes for Influence Maximization (Student Abstract)

Inder Khatri
Aaryan Gupta
Arjun Choudhry
Aryan Tyagi
Dinesh Kumar Vishwakarma
Mukesh Prasad

Social networks have enabled user-specific advertisements and recommendations on their platforms, which puts a significant focus on Influence Maximisation (IM) for target advertising and related tasks. The aim is to identify nodes in the network which can maximize the spread of information through a diffusion cascade. We propose a community structures-based approach that employs K-Shell algorithm with community structures to generate a score for the connections between seed nodes and communities. Further, our approach employs entropy within communities to ensure the proper spread of information within the communities. We validate our approach on four publicly available networks and show its superiority to four state-of-the-art approaches while still being relatively efficient.

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