http://repository.iiitd.edu.in/xmlui/handle/123456789/1654 Year-2024 Sun, 23 Aug 2026 11:52:38 GMT 2026-08-23T11:52:38Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1993 Title: Accessing trust in mental health counselling conversation Authors: Agarwal, Ayush; Akhtar, Md. Shad (Advisor) Abstract: Mental health has emerged as the most critical global healthcare concern which is ac celerated by recent lifestyle changes, evolving individual aspirations, and social media. The use of AI in mental health has been useful in every aspect and stage of mental health- from monitoring, assistance and evaluation to providing a cost-effective, scalable, and qualitative improvement of mental health therapeutic outcomes. However, there is a serious lack of research and assistive tools in providing real-time patient feedback to measure aspects and overall therapeutic outcomes of psychotherapy. To this end, we propose a novel metric of trust in mental health dialogue systems. Specifically, every patient’s utterance in a counseling dialogue is scored on a scale that signals the degree of trust developed in the patient towards the therapist at any point in the therapy ses sion. The contribution of this work is twofold (a) we provide a concretized definition of the novel metric of trust in mental health dialogue systems and a clear annotation framework that scores the strength of trust developed between a patient and therapist during counseling. (b) We discuss various strategies to model trust correctly and finally model it as a knowledge-guided time-series forecasting problem. We perform exten sive experimentation and benchmark trust on various classical and modern methods of time-series forecasting, with our best model achieving an MSE loss of 0.0017. Thu, 18 Jul 2024 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1993 2024-07-18T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1965 Title: Ship marine strategy database access using natural language: an application of LLM-based text-to-SQL model Authors: Ghorai, Arunoday; Goyal, Vikram (Advisor) Abstract: The growing reliance on relational databases across industries and the ability to efficiently query and extract from a structured database has become a crucial skill in the industry. However, the Complexity of SQL Syntax creates a barrier for non-technical uses limiting their ability to interact with databases effectively. Natural Language to SQL (NL-to-SQL) query generation performs a critical task in bridging gap between non-technical users and relational databases and enables intuitive data interaction with out any need for SQL expertise. This thesis first explores various Text-to-SQL approaches, leveraging both proprietary model like Open AI’s GPT-4 and open-source models like RESDSQL, focusing on their performance across benchmark datasets like Spider, CoSQL and SPARC. Additionally, two datasets, MORD and CMEC are prepared from the real world use cases to highlight unique challenges such as hierarchical data structures, string matching operations, and privacy issues. The MORD dataset was queried using GPT-4 integrated with LangChain, to showcase natural language interaction with data and the usability of proprietary models without any tuning to domain specific dataset. Meanwhile the CMEC dataset is a privately curated dataset and access to it needs to be confidential. So we use open source models like RESDSQL that run on local server in order to minimize leakage. The dataset is pre-processed into a relational schema, and RESDSQL is fine tuned on curated NL to SQL pairs to improve performance. String matching techniques are applied to prepare better prompts in order to further enhance the results generated by the model. Sun, 01 Dec 2024 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1965 2024-12-01T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1788 Title: Detection to interpretation: advancing tabular data processing with multimodal AI Authors: Bhuyan, Pijush; Shah, Rajiv Ratn (Advisor) Abstract: Tables are the most common form of structured data found in documents. Proper interpretation of such raw tabular data by computer systems remains an open challenge. We take a deep dive into document intelligence - which includes table detection, table reconstruction and table structure interpretation by AI models. Firstly, we handle domain adaptation in table detection. Pre-trained table detection models have displayed poor results when the target domain varied from the source. We resolve this by building a domain invariant table detection dataset where we inject additional noisy synthetic detection data. Empirical tests show that training a detection model on synthetic data displays a significantly lower drop in performance when tested on out-of-distribution datasets. Following this, we build a fast,yet efficient, end-to-end pipeline for Table-OCR. It reconstructs the table structure and content from raw detection crops and converts them into computer-storable text format. Finally, we design a comprehensive benchmark suite of tests to test the table structure understanding capabilities and limitations of existing Large Language models (LLMs) and Vision Language Models (VLMs) using both text and image modalities. The vision component of VLMs is found to be a bottleneck in multi-modal table interpretability. We work with a light-weight, yet efficient model-agnostic adapter module which injects positional information into the image modality through positional embeddings during model training. We also design a novel pre-training task for image-text alignment for open-source VLMs and study the change in model performance while interpreting visual tabular data. We also study the feasibility and future scope for true multimodal table understanding - interpreting tabular data from both image and text modalities for reasoning. Sat, 21 Dec 2024 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1788 2024-12-21T00:00:00Z http://repository.iiitd.edu.in/xmlui/handle/123456789/1695 Title: Optimising serialisation for cloud applications Authors: Nayak, Siddharth; Shah, Rinku (Advisor) Abstract: Serialisation latency is a significant concern in modern cloud applications that leverage the microservice paradigm. A cloud service request typically traverses a sequence of microservices across nodes, increasing latency due to (de) serialisation overhead at every hop. The serialisation process comprises memory allocation, data encoding, and data copy. Observations from existing benchmarking results show that the data copy operation dominates the overall (de) serialisation cost. Existing serialisation libraries follow a two-copy technique — (1) the application copies the encoded data into a serialised buffer, and (2) the serialised data is copied to the NIC’s device memory. To reduce serialisation latency, researchers have proposed (1) kernel bypass techniques that eliminate data copy, (2) use of hardware acceleration solutions, and (3) wire format optimisations. However, kernel bypass solutions have security concerns and cannot be deployed in public cloud networks, and hardware acceleration solutions depend on specialised hardware. We propose designing and implementing a one-copy serialisation library, which leverages the scatter-gather I/O technique provided by the standard POSIX library for data movement. Our solution does not require special hardware support or any specialised network stack. Our design relies on the Linux network stack; there are no security concerns, making it usable in public and private clouds. Tue, 21 May 2024 00:00:00 GMT http://repository.iiitd.edu.in/xmlui/handle/123456789/1695 2024-05-21T00:00:00Z