Fenil Bardoliya

2026

1. Program-of-Thought Reveals LLM Abstraction Ceilings

   Mike Zhou, Fenil Bardoliya, Vivek Gupta, and Dan Roth

   In Findings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2026

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2025

1. 

   The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs

   Tejas Anvekar, Fenil Bardoliya, Pavan K. Turaga, Chitta Baral, and Vivek Gupta

   2025

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2. 

   SPORTSQL: An Interactive System for Real-Time Sports Reasoning and Visualization

   Sebastian Martinez, Naman Ahuja, Fenil Bardoliya, Suparno Roy Chowdhury, Chris Bryan, and 1 more author

   In Proceedings of The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations, Dec 2025

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   We present a modular, interactive system, SPORTSQL, for natural language querying and visualization of dynamic sports data, with a focus on the English Premier League (EPL). The system translates user questions into executable SQL over a live, temporally indexeddatabase constructed from real-time Fantasy Premier League (FPL) data. It supports both tabular and visual outputs, leveraging symbolic reasoning capabilities of Large Language Models (LLMs) for query parsing, schema linking, and visualization selection. To evaluate system performance, we introduce the Dynamic Sport Question Answering Benchmark (DSQABENCH), comprising 1,700+ queries annotated with SQL programs, gold answers, and database snapshots. Our demo highlights how non-expert users can seamlessly explore evolving sports statistics through a natural, conversational interface.

3. 

   Map&Make: Schema Guided Text to Table Generation

   Naman Ahuja, Fenil Bardoliya, Chitta Baral, and Vivek Gupta

   In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2025

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   Transforming dense, unstructured text into interpretable tables—commonly referred to as Text-to-Table generation—is a key task in information extraction. Existing methods often overlook what complex information to extract and how to infer it from text. We present Map&Make, a versatile approach that decomposes text into atomic propositions to infer latent schemas, which are then used to generate tables capturing both qualitative nuances and quantitative facts. We evaluate our method on three challenging datasets: Rotowire, known for its complex, multi-table schema; Livesum which requires numerical aggregation; and Wiki40 which require open text extraction from mulitple domains. By correcting hallucination errors in Rotowire, we also provide a cleaner benchmark. Our method shows significant gains in both accuracy and interpretability across comprehensive comparative and referenceless metrics. Finally, ablation studies highlight the key factors driving performance and validate the utility of our approach in structured summarization. Code and data are available at: https://coral-lab-asu.github.io/map-make.

2024

1. 

   Evaluating Multimodal Large Language Models Across Distribution Shifts and Augmentations

   Aayush Atul Verma, Amir Saeidi, Shamanthak Hegde, Ajay Therala, Fenil Denish Bardoliya, and 6 more authors

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Jun 2024

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