Wrick Talukdar

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 alignment with human values has become a critical challenge. This paper presents a novel framework for contextual grounding in textual models, with a particular emphasis on the Context Representation stage. Our approach aims to enhance the reliability and ethical alignment of these models through a comprehensive, context-aware methodology. By explicitly capturing and representing relevant situational, cultural, and ethical contexts in a machine-readable format, we lay the foundation for anchoring a model's behavior within these contexts. Our approach leverages techniques from knowledge representation and reasoning, such as ontologies, semantic web technologies, and logic-based formalisms. We evaluate our framework on real-world textual datasets, demonstrating its effectiveness in improving model performance, fairness, and alignment with human expectations, while maintaining high accuracy. Furthermore, we discuss the other key components of the framework, including context-aware encoding, context-aware learning, interpretability and explainability, and continuous monitoring and adaptation. This research contributes to the growing body of work on responsible AI, offering a practical approach to developing more reliable, trustworthy, and ethically-aligned language models. Our findings have significant implications for the deployment of LLMs in sensitive domains such as healthcare, legal systems, and social services, where contextual understanding is paramount.

Anjanava Biswas, Wrick Talukdar

The AI era has ushered in Large Language Models (LLM) to the technological forefront, which has been much of the talk in 2023, and is likely to remain as such for many years to come. LLMs are the AI models that are the power house behind generative AI applications such as ChatGPT. These AI models, fueled by vast amounts of data and computational prowess, have unlocked remarkable capabilities, from human-like text generation to assisting with natural language understanding (NLU) tasks. They have quickly become the foundation upon which countless applications and software services are being built, or at least being augmented with. However, as with any groundbreaking innovations, the rise of LLMs brings forth critical safety, privacy, and ethical concerns. These models are found to have a propensity to leak private information, produce false information, and can be coerced into generating content that can be used for nefarious purposes by bad actors, or even by regular users unknowingly. Implementing safeguards and guardrailing techniques is imperative for applications to ensure that the content generated by LLMs are safe, secure, and ethical. Thus, frameworks to deploy mechanisms that prevent misuse of these models via application implementations is imperative. In this study, wepropose a Flexible Adaptive Sequencing mechanism with trust and safety modules, that can be used to implement safety guardrails for the development and deployment of LLMs.

Wrick Talukdar

The gas station of the future is poised to transform from a simple fuel dispensing center into an intelligent retail hub, driven by advancements in Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT). This paper explores how technology is reshaping the retail downstream sector while briefly addressing the upstream and midstream segments. By leveraging AI/ML for predictive analytics, dynamic pricing, personalized customer engagement, and IoT for real-time monitoring and automation, the future gas station will redefine the fuel retail experience. Additionally, this paper incorporates statistics, AI/ML core technical concepts, mathematical formulations, case studies, and a proposed framework for a fully autonomous gas station.

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 accuracy of these models can be significantly affected by document in-plane rotation, also known as skew, a common issue in real-world scenarios for scanned documents. This study investigates the impact of document skew on the data extraction accuracy of three state-of-the-art multi-modal LLMs: Anthropic Claude V3 Sonnet, GPT-4-Turbo, and Llava:v1.6. We focus on extracting specific entities from synthetically generated sample documents with varying degrees of skewness. The results demonstrate that document skew adversely affects the data extraction accuracy of all the tested LLMs, with the severity of the impact varying across models. We identify the safe in-plane rotation angles (SIPRA) for each model and investigate the effects of skew on model hallucinations. Furthermore, we explore existing skew detection and correction mechanisms and discuss their potential limitations. We propose alternative approaches, including developing new multi-modal architectures that are inherently more robust to document skew and incorporating skewing techniques during the pre-training phase of the models. Additionally, we highlight the need for more comprehensive testing on a wider range of document quality and conditions to fully understand the challenges and opportunities associated with using multi-modal LLMs for information extraction in real-world scenarios.

Anjanava Biswas, Wrick Talukdar

Accurate and comprehensive clinical documentation is crucial for delivering high-quality healthcare, facilitating effective communication among providers, and ensuring compliance with regulatory requirements. However, manual transcription and data entry processes can be time-consuming, error-prone, and susceptible to inconsistencies, leading to incomplete or inaccurate medical records. This paper proposes a novel approach to augment clinical documentation by leveraging synthetic data generation techniques to generate realistic and diverse clinical transcripts. We present a methodology that combines state-of-the-art generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), with real-world clinical transcript and other forms of clinical data to generate synthetic transcripts. These synthetic transcripts can then be used to supplement existing documentation workflows, providing additional training data for natural language processing models and enabling more accurate and efficient transcription processes. Through extensive experiments on a large dataset of anonymized clinical transcripts, we demonstrate the effectiveness of our approach in generating high-quality synthetic transcripts that closely resemble real-world data. Quantitative evaluation metrics, including perplexity scores and BLEU scores, as well as qualitative assessments by domain experts, validate the fidelity and utility of the generated synthetic transcripts. Our findings highlight synthetic data generation's potential to address clinical documentation challenges, improving patient care, reducing administrative burdens, and enhancing healthcare system efficiency.

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-scale labeled datasets, which can be costly and time-consuming to obtain. Conversely, unsupervised learning techniques can leverage abundant unlabeled text data to learn rich representations, but they do not directly optimize for specific NLP tasks. This paper presents a novel hybrid approach that synergizes unsupervised and supervised learning to improve the accuracy of NLP task modeling. While supervised models excel at specific tasks, they rely on large labeled datasets. Unsupervised techniques can learn rich representations from abundant unlabeled text but don't directly optimize for tasks. Our methodology integrates an unsupervised module that learns representations from unlabeled corpora (e.g., language models, word embeddings) and a supervised module that leverages these representations to enhance task-specific models. We evaluate our approach on text classification and named entity recognition (NER), demonstrating consistent performance gains over supervised baselines. For text classification, contextual word embeddings from a language model pretrain a recurrent or transformer-based classifier. For NER, word embeddings initialize a BiLSTM sequence labeler. By synergizing techniques, our hybrid approach achieves SOTA results on benchmark datasets, paving the way for more data-efficient and robust NLP systems.