36 citations · 42 across the 9 of their papers we have counts for
9 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…
Neurosymbolic Retrievers for Retrieval-augmented Generation
Yash Saxena, Manas Gaur
Retrieval Augmented Generation (RAG) has made significant strides in overcoming key limitations of large language models, such as hallucination, lack of contextual grounding, and i…
IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation
Yash Saxena, Ankur Padia, Kalpa Gunaratna +1
Interpretability in black-box dense retrievers remains a central challenge in Retrieval-Augmented Generation (RAG). Understanding how queries and documents semantically interact is…
Generation-Time vs. Post-hoc Citation: A Holistic Evaluation of LLM Attribution
Yash Saxena, Raviteja Bommireddy, Ankur Padia +1
Trustworthy Large Language Models (LLMs) must cite human-verifiable sources in high-stakes domains such as healthcare, law, academia, and finance, where even small errors can have…
Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains
Yash Saxena, Ankur Padia, Mandar S Chaudhary +3
Retrieval-Augmented Generation (RAG) systems deployed in sensitive domains must provide interpretable evidence selection and robust safeguards against data poisoning, yet current a…
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…
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…
Evaluating Consistency and Reasoning Capabilities of Large Language Models
Yash Saxena, Sarthak Chopra, Arunendra Mani Tripathi
Large Language Models (LLMs) are extensively used today across various sectors, including academia, research, business, and finance, for tasks such as text generation, summarizatio…
Deploying and Evaluating LLMs to Program Service Mobile Robots
Zichao Hu, Francesca Lucchetti, Claire Schlesinger +5
Recent advancements in large language models (LLMs) have spurred interest in using them for generating robot programs from natural language, with promising initial results. We inve…