8 papers
TabJoinBench: A Benchmark for Joinable Table Discovery
Sandipan De, Jin Wang, Vivek Gupta
Join discovery aims to identify tables from large data repositories that can augment a query table with complementary information, enabling downstream tasks such as data exploratio…
DraftTrace: A Multi-View Analytics Environment for AI-Integrated Writing
Divyansh Chandarana, Sandipan De, Vivek Gupta
Generative AI has changed how students produce writing assignments. The final artifact is no longer sufficient to understand the process through which it was produced. We introduce…
JobMatchAI-An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI
Mayank Vyas, Abhijit Chakraborty, Vivek Gupta
Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes. Most systems act as keyword filters, fa…
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…
The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs
Tejas Anvekar, Fenil Bardoliya, Pavan K. Turaga +2
Recent advances in multimodal large language models (MLLMs) have yielded increasingly powerful models, yet their perceptual capacities remain poorly characterized. In practice, mos…
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…