Visually Grounded Document Question Answering often lacks robust, end-to-end solutions capable of handling complex, multi-answer queries without reliance on ad-hoc processing. In this work, we propose two turnkey LLM architectures to address this gap. We first introduce a single-head architecture where coordinates are represented as special tokens within the unified vocabulary. While structurally robust, this approach suffers from the limitations of discrete supervision; to address this, we propose a novel “softening token” method that enables differentiable Mean-Squared-Error loss over token probabilities. Although this significantly improves visual grounding, the spatial precision remains bound by discretization. Consequently, we propose a second solution: a dual-head architecture that alternates between text generation and regression-based bounding box prediction. This method offers high spatial precision via a regression head, further stabilized by our introduction of an Intersection-over-Union loss. Finally, by combining the single head model’s structural robustness with the high precision of the dual head model, we propose an ensemble method that yields significant performance gains beyond each of individual components.

Large language models (LLMs) are increasingly being used to generate synthetic datasets for the evaluation and training of downstream models. However, prior work has noted that such generated data lacks diversity. In this paper, we propose VOYAGER, a novel principled approach to generate diverse datasets. Our approach is iterative and directly optimizes a mathematical quantity that optimizes the diversity of the dataset using the machinery of determinantal point processes. Furthermore, our approach is training-free, applicable to closed-source models, and scalable. In addition to providing theoretical justification for the working of our method, we also demonstrate through comprehensive experiments that VOYAGER improves diversity by 1.5-𝟑 times compared to popular baseline approaches.