12 papers
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…
TabReX : Tabular Referenceless eXplainable Evaluation
Tejas Anvekar, Junha Park, Aparna Garimella +1
Evaluating the quality of tables generated by large language models (LLMs) remains an open challenge: existing metrics either flatten tables into text, ignoring structure, or rely…
TabXEval: Why this is a Bad Table? An eXhaustive Rubric for Table Evaluation
Vihang Pancholi, Jainit Bafna, Tejas Anvekar +2
Evaluating tables qualitatively and quantitatively poses a significant challenge, as standard metrics often overlook subtle structural and content-level discrepancies. To address t…
Rethinking Information Synthesis in Multimodal Question Answering A Multi-Agent Perspective
Krishna Singh Rajput, Tejas Anvekar, Chitta Baral +1
Recent advances in multimodal question answering have primarily focused on combining heterogeneous modalities or fine-tuning multimodal large language models. While these approache…
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…