Papers by Yashas Malur Saidutta

2 papers
VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs (2026.acl-long)

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Challenge: Large language models (LLMs) are used to generate synthetic datasets but lack diversity . prior work has noted that such generated data lacks diversity - a problem that requires domain expertise.
Approach: They propose a principled approach that optimizes a mathematical quantity that optimize the diversity of the dataset using determinantal point processes.
Outcome: The proposed method improves diversity by 1.5-3 times compared to baseline approaches.
Switching Heads and Softening Tokens: Turnkey Solutions to Visually Grounded Document QA (2026.findings-acl)

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Challenge: Document Question Answering lacks robust, end-to-end solutions capable of handling complex, multi-answer queries without reliance on ad-hoc processing.
Approach: They propose a single-head architecture where coordinates are represented as special tokens within the unified vocabulary.
Outcome: The proposed architectures improve visual grounding but lack spatial precision bound by discretization.

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