Kaustubh Ponkshe

Kaustubh Ponkshe, Venkatapathy Subramanian, Natwar Modani et al.

Most state-of-the-art techniques for Language Models (LMs) today rely on transformer-based architectures and their ubiquitous attention mechanism. However, the exponential growth in computational requirements with longer input sequences confines Transformers to handling short passages. Recent efforts have aimed to address this limitation by introducing selective attention mechanisms, notably local and global attention. While sparse attention mechanisms, akin to full attention in being Turing-complete, have been theoretically established, their practical impact on pre-training remains unexplored. This study focuses on empirically assessing the influence of global attention on BERT pre-training. The primary steps involve creating an extensive corpus of structure-aware text through arXiv data, alongside a text-only counterpart. We carry out pre-training on these two datasets, investigate shifts in attention patterns, and assess their implications for downstream tasks. Our analysis underscores the significance of incorporating document structure into LM models, demonstrating their capacity to excel in more abstract tasks, such as document understanding.

Priya Mishra, Suraj Racha, Kaustubh Ponkshe et al.

Question Answering (QA) is an important part of tasks like text classification through information gathering. These are finding increasing use in sectors like healthcare, customer support, legal services, etc., to collect and classify responses into actionable categories. LLMs, although can support QA systems, they face a significant challenge of insufficient or missing information for classification. Although LLMs excel in reasoning, the models rely on their parametric knowledge to answer. However, questioning the user requires domain-specific information aiding to collect accurate information. Our work, GUIDEQ, presents a novel framework for asking guided questions to further progress a partial information. We leverage the explainability derived from the classifier model for along with LLMs for asking guided questions to further enhance the information. This further information helps in more accurate classification of a text. GUIDEQ derives the most significant key-words representative of a label using occlusions. We develop GUIDEQ's prompting strategy for guided questions based on the top-3 classifier label outputs and the significant words, to seek specific and relevant information, and classify in a targeted manner. Through our experimental results, we demonstrate that GUIDEQ outperforms other LLM-based baselines, yielding improved F1-Score through the accurate collection of relevant further information. We perform various analytical studies and also report better question quality compared to our method.