2 citations · 5 across the 6 of their papers we have counts for
6 papers
NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models
Deepa Tilwani, Yash Saxena, Ankur Padia +2
Large Language Models (LLMs) have transformed natural language processing, but their lack of interpretable reasoning and tendency to hallucinate pose significant challenges for leg…
Can LLMs Obfuscate Code? A Systematic Analysis of Large Language Models into Assembly Code Obfuscation
Seyedreza Mohseni, Seyedali Mohammadi, Deepa Tilwani +5
Malware authors often employ code obfuscations to make their malware harder to detect. Existing tools for generating obfuscated code often require access to the original source cod…
Neurosymbolic AI approach to Attribution in Large Language Models
Deepa Tilwani, Revathy Venkataramanan, Amit P. Sheth
Attribution in large language models (LLMs) remains a significant challenge, particularly in ensuring the factual accuracy and reliability of the generated outputs. Current methods…
Deep Jansen-Rit Parameter Inference for Model-Driven Analysis of Brain Activity
Deepa Tilwani, Christian O'Reilly
Accurately modeling effective connectivity (EC) is critical for understanding how the brain processes and integrates sensory information. Yet, it remains a formidable challenge due…
Abstention vs. Hallucination: Benchmarking LLM Source Attribution for Scientific Citations
Deepa Tilwani, Yash Saxena, Seyedali Mohammadi +5
Large language models (LLMs) increasingly generate citation-backed responses, yet citation hallucination remains a major challenge for trustworthy scientific information access. We…
A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression
Sumit Dalal, Deepa Tilwani, Kaushik Roy +4
The lack of explainability using relevant clinical knowledge hinders the adoption of Artificial Intelligence-powered analysis of unstructured clinical dialogue. A wealth of relevan…