Papers by Siddharth Parekh

2 papers
AliGATr: Graph-based layout generation for form understanding (2024.findings-emnlp)

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Challenge: State of the art forms understanding models often rely on poorly calibrated output probabilities and low performance on relation extraction tasks.
Approach: They propose a graph-based model that uses a generative objective to represent complex grid-like layouts that are often found in forms.
Outcome: The proposed model performs better on the KIE and RE tasks and is more accurate than existing models.
Where is this coming from? Making groundedness count in the evaluation of Document VQA models (2025.findings-naacl)

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Challenge: Document Visual Question Answering (VQA) models have come close to or matching human performance on some benchmarks.
Approach: They propose a method that accounts for the semantic and multimodal groundedness of a model’s outputs and can be parameterized so that users can configure the score according to their preferences.
Outcome: The proposed method produces scores that are a better indicator of a model’s robustness and tends to give higher rewards to better-calibrated answers.

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