12 citations · 14 across the 14 of their papers we have counts for
10 papers · 1 filter
SCOPE:Planning for Hybrid Querying over Clinical Trial Data
Suparno Roy Chowdhury, Manan Roy Choudhury, Tejas Anvekar +5
We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through normalization, classificati…
FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use
Suparno Roy Chowdhury, Tejas Anvekar, Manan Roy Choudhury +5
Clinicians exploring oncology trial repositories often need ad-hoc, multi-constraint queries over biomarkers, endpoints, interventions, and time, yet writing SQL requires schema ex…
ViTaB-A: Evaluating Multimodal Large Language Models on Visual Table Attribution
Yahia Alqurnawi, Preetom Biswas, Anmol Rao +3
Multimodal Large Language Models (mLLMs) are often used to answer questions in structured data such as tables in Markdown, JSON, and images. While these models can often give corre…
TraceBack: Multi-Agent Decomposition for Fine-Grained Table Attribution
Tejas Anvekar, Junha Park, Rajat Jha +4
Question answering (QA) over structured tables requires not only accurate answers but also transparency about which cells support them. Existing table QA systems rarely provide fin…
DoPE: Decoy Oriented Perturbation Encapsulation Human-Readable, AI-Hostile Documents for Academic Integrity
Ashish Raj Shekhar, Shiven Agarwal, Priyanuj Bordoloi +3
Multimodal Large Language Models (MLLMs) can directly consume exam documents, threatening conventional assessments and academic integrity. We present DoPE (Decoy-Oriented Perturbat…
Integrity Shield A System for Ethical AI Use & Authorship Transparency in Assessments
Ashish Raj Shekhar, Shiven Agarwal, Priyanuj Bordoloi +3
Large Language Models (LLMs) can now solve entire exams directly from uploaded PDF assessments, raising urgent concerns about academic integrity and the reliability of grades and c…