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Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracity
Wrick Talukdar, Anjanava Biswas
As Large Language Models (LLMs) become increasingly sophisticated and ubiquitous in natural language processing (NLP) applications, ensuring their robustness, trustworthiness, and…
Robustness of Structured Data Extraction from In-plane Rotated Documents using Multi-Modal Large Language Models (LLM)
Anjanava Biswas, Wrick Talukdar
Multi-modal large language models (LLMs) have shown remarkable performance in various natural language processing tasks, including data extraction from documents. However, the accu…
Enhancing Clinical Documentation with Synthetic Data: Leveraging Generative Models for Improved Accuracy
Anjanava Biswas, Wrick Talukdar
Accurate and comprehensive clinical documentation is crucial for delivering high-quality healthcare, facilitating effective communication among providers, and ensuring compliance w…
Synergizing Unsupervised and Supervised Learning: A Hybrid Approach for Accurate Natural Language Task Modeling
Wrick Talukdar, Anjanava Biswas
While supervised learning models have shown remarkable performance in various natural language processing (NLP) tasks, their success heavily relies on the availability of large-sca…