Papers by Tejas Anvekar
TabReX: Tabular Referenceless eXplainable Evaluation (2026.acl-long)
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| Challenge: | Existing metrics for evaluating the quality of tables generated by large language models flatten tables into text, ignoring structure or relying on fixed references that limit generalization. |
| Approach: | They propose a reference-less framework for evaluating tabular generation via graph-based reasoning . tabReX converts source text and generated tables into canonical knowledge graphs . |
| Outcome: | The proposed framework provides a high correlation with expert rankings and stable under harder perturbations. |
Integrity Shield A System for Ethical AI Use & Authorship Transparency in Assessments (2026.eacl-demo)
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| Challenge: | Existing methods for detecting authorship and short answers are ineffective when students query proprietary black-box systems using instructor-provided documents. |
| Approach: | They propose a document-layer watermarking system that embeds schema-aware, item-level watermarks into assessment PDFs while keeping their human-visible appearance unchanged. |
| Outcome: | The proposed system achieves high prevention and detection reliability across 30 question papers spanning STEM, humanities, and medical reasoning. |
FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use (2026.acl-demo)
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Suparno Roy Chowdhury, Tejas Anvekar, Manan Roy Choudhury, Muhammad Ali Khan, Kaneez Zahra Rubab Khakwani, M Bassam Sonbol, Irbaz Bin Riaz, Vivek Gupta
| Challenge: | Clinical trial databases are central to modern oncology research and drug development. |
| Approach: | FD-NL2SQL is a schema-aware clinical NL2sql assistant for SQLite-based oncology databases . it decomposes a natural-language question into predicate-level sub-questions and synthesizes executable SQL . |
| Outcome: | FD-NL2SQL synthesizes SQL based on decomposition, retrieved exemplars, and schema . clinical trial databases are central to modern oncology research and drug development . |
TabXEval: Why this is a Bad Table? An eXhaustive Rubric for Table Evaluation (2025.findings-acl)
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| Challenge: | a rubric-based evaluation framework for tables often overlooks subtle structural and content-level discrepancies. |
| Approach: | They propose a rubric-based evaluation framework that integrates structural descriptors with contextual signals to enable more precise and consistent table comparison. |
| Outcome: | The proposed framework is based on a multi-domain benchmark with human annotations and perturbations. |