Papers by Anshul Singh
MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space (2025.findings-emnlp)
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| Challenge: | Existing benchmarks address single tables or non-visual data, leaving a critical gap . MTabVQA comprises 3,745 complex question-answer pairs . |
| Approach: | They propose a benchmark specifically designed for multi-tabular visual question answering that measures the ability to parse diverse table images and correlate information across them. |
| Outcome: | The proposed benchmarks show that fine-tuning VLMs with MTabVQA-Instruct significantly improves their reasoning abilities. |
MIMOQA: Multimodal Input Multimodal Output Question Answering (2021.naacl-main)
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| Challenge: | Multimodal research has picked up significantly in the space of question answering with the task being extended to visual question answering, charts question answering as well as multimodal input question answering. |
| Approach: | They propose a multimodal question-answering task that produces a unimodal textual output as the answer through human experiments. |
| Outcome: | The proposed framework outperforms existing frameworks on both automatic and human metrics. |
JTPRO: A Joint Tool–Prompt Reflective Optimization Framework for Language Agents (2026.findings-acl)
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Sandip Ghoshal, Anshul Mittal, Jyotika Singh, Miguel Ballesteros, Weiyi Sun, Fang Tu, Shailender Singh, Yassine Benajiba, Fahad Shah, Sujeeth Bharadwaj, Sujith Ravi, Dan Roth
| Challenge: | Large language model agents struggle with ambiguous tool descriptions and underspecified tool schemas that ignore tool-specific nuances. |
| Approach: | They propose a framework for improving tool-calling reliability in trace-supervised settings by rolling out-driven reflection. |
| Outcome: | The proposed framework outperforms baselines and reflective prompt optimizers by 5%–20% on OSR. |