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20212025
most citedLLM-Generated Black-box Explanations Can Be Adversarially Helpful

9 citations · 11 across the 4 of their papers we have counts for

collaborators

5 papers

cs.IR2025

E-CARE: An Efficient LLM-based Commonsense-Augmented Framework for E-Commerce

Ge Zhang, Rohan Deepak Ajwani, Yaochen Hu +5

Finding relevant products given a user query is pivotal to an e-commerce platform, as it can drive shopping behavior and generate revenue. The challenge lies in accurately predicti…

cs.AI2025

Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs

Mohammad Ali Alomrani, Yingxue Zhang, Derek Li +14

Large language models (LLMs) have rapidly progressed into general-purpose agents capable of solving a broad spectrum of tasks. However, current models remain inefficient at reasoni…

cs.CL2024★ 9 cited

LLM-Generated Black-box Explanations Can Be Adversarially Helpful

Rohan Ajwani, Shashidhar Reddy Javaji, Frank Rudzicz +1

Large Language Models (LLMs) are becoming vital tools that help us solve and understand complex problems by acting as digital assistants. LLMs can generate convincing explanations,…

cs.CL2024★ 2 cited

Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation

Rohan Deepak Ajwani, Zining Zhu, Jonathan Rose +1

Transformer-based Large Language Models (LLMs) have shown exceptional language generation capabilities in response to text-based prompts. However, controlling the direction of gene…

cs.NE2021

Sparse Distributed Memory using Spiking Neural Networks on Nengo

Rohan Deepak Ajwani, Arshika Lalan, Basabdatta Sen Bhattacharya +1

We present a Spiking Neural Network (SNN) based Sparse Distributed Memory (SDM) implemented on the Nengo framework. We have based our work on previous work by Furber et al, 2004, i…