Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 256 for “"Large Language Models (llms)"”.
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Linguistic Deception Detection – Models, Domains, Behaviors, Stylistic Patterns to Large Language Models (LLMs)
Deception in language—ranging from fake news and spam to phishing and rumor—has long been a tool for manipulation, exploiting linguistic ambiguity and psychological triggers to mislead readers. Deception spanned varied domains, yet shared common traits, which enabled the development of …
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Leveraging Large Language Models (LLMs) for Automated Extraction and Processing of Complex Ordering Forms
… documents. This thesis explores the use of Large Language Models (LLMs) to automate the extraction and processing of ordering forms and procurement documents in collaboration with SiliconExperts. These documents contain complex codes used in electronic component procurement, which guide the …
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Η αξιοποίηση των Μεγάλων Γλωσσικών Μοντέλων (Large Language Models – LLMs) στη διδασκαλία/εκμάθηση Αγγλικών για Επαγγελματικούς Σκοπούς (ESP): Συστηματική Βιβλιογραφική Ανασκόπηση
… την αξιοποίηση των Μεγάλων Γλωσσικών Μοντέλων (Large Language Models – LLMs) στη διδασκαλία και εκμάθηση Αγγλικών για Επαγγελματικούς Σκοπούς (English for Specific Purposes – ESP), με ιδιαίτερη έμφαση στο Business ESP. Σκοπός της εργασίας είναι η χαρτογράφηση των κυρίαρχων εφαρμογών των LLMs, η …
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DOCUMENT-LEVEL RELATION EXTRACTION AND TEMPORAL REASONING WITH LARGE LANGUAGE MODELS
Large Language Models (LLMs) have become the backbone models for many natural language processing (NLP) tasks. In this thesis, we study the applications and limitations of LLMs in two aspects: (1) Document-level Relation Extraction (DocRE), and (2) Temporal Reasoning.
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Unsupervised Time Series Anomaly Detection Using Time Series Foundational Models
… series anomaly detection, including statistical models like ARIMA and deep learning methods, have proven effective but often require an extensive training phase, which can be both data and time-consuming. In recent years, the emergence of foundational models, including large language models …
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Mitigating LLM Hallucination in the Banking Domain
Large Language Models (LLMs) offer significant potential in the banking sector, particularly for applications such as fraud detection, credit approval, and enhancing customer experience. However, their tendency to "hallucinate"—generating plausible but inaccurate information—poses a critical …
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Promoting a healthy and comprehensive diet through theory-driven large language models-based agents
… in health promotion counseling to improve Large Language Models (LLMs) for recognizing and responding to diverse motivational states. The study identifies the limited capabilities of Large Language Models (LLMs) in providing information tailored to the motivational readiness for change …
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Goal Inference from Open-Ended Dialog
… goals and preferences efficiently and robustly. Large Language Models (LLMs) are often used as they allow for opportunities for rich and open-ended dialog type interaction between the human and agent to accomplish tasks according to human preferences. In this thesis, we argue that for embodied …
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Truthfulness in Large Language Models
Large language models (LLMs) have been experiencing a rapid rise in utility, accessibility, and popularity, but there are still many areas in which they can improve. One such area for improvement is their truthfulness. We seek to improve the truthfulness of LLMs by probing their internal …
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Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager
The growing importance of large language models (LLMs) in daily life has heightened awareness and concerns about the fact that LLMs exhibit many of the same biases as their creators. In the context of hiring decisions, we quantify the degree to which LLMs perpetuate biases originating from their …
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Investigating Model Editing for Unlearning in Large Language Models
… With the increasing usage and influence of large language models (LLMs) that are trained on personal data, a question of how to implement the removal of information within these models arises. In addition, large language models (LLMs) are trained on a large corpus of data that is usually …
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Transforming SDOH Screening: Towards a General Framework for Transformer-based Prediction of Social Determinants of Health
… This research explores the potential of Large Language Models (LLMs) for automated SDOH identification from patient notes. We propose a general framework for SDOH screening that is simple and straightforward. We leverage existing SDOH datasets, adapting and combining them to create a more …
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Provably reliable machine learning systems
… iterative deployment cycles. Simultaneously, Large Language Models (LLMs) in compound systems frequently generate outputs that violate syntactic and semantic specifications, leading to cascading failures in automated workflows. Thus, developing reliability techniques for machine learning …
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Blueprinting AI Economics: Cost Assessment Framework for Business Stakeholders to Navigate Key Aspects in Prompt Engineering, Prompt Automation, and Fine-tuning LLMs
The rapid proliferation of large language models (LLMs) has led to an intense focus on achieving unprecedented performance benchmarks, often at the expense of considering the substantial computational costs involved. This oversight is compounded by the lack of robust, academically grounded …
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Efficient Adaptation of Large Language Models in Natural Language Processing
The rapid growth of Large Language Models (LLMs) has significantly improved performance across a wide range of Natural Language Processing (NLP) tasks, including Information Retrieval (IR). Despite their strong generalisation capabilities, LLMs still require domain- and task-specific fine-tuning to …
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Improving Accuracy Predictions of Companion Classifiers for LLM Routing
The increasing versatility of Large Language Models (LLMs) calls for developing effective routing systems to match tasks with the most suitable models, balancing accuracy and computational cost. This research introduces a novel meta-cascade routing framework that combines meta-routing, where a …
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Design and development of an LLM-based framework for synthetic data generation
… for generating synthetic data using fine-tuned Large Language Models (LLMs) and Generative AI techniques. The framework generates realistic, domain-specific datasets that preserve complex patterns while ensuring privacy through differential privacy methods. It can create synthetic data from …
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CLOSED-LOOP SCALING: AUTONOMOUS IMPROVEMENT OF LLM AND LVLM REASONING
… exhaustion, sustaining the improvement of large language models (LLMs) and large vision--language models (LVLMs) demands a paradigm shift. This thesis proposes automatic scaling: a closed-loop framework in which models autonomously improve through their own computation via three layers. …
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Copilot Tutor: Automated Software Engineering Practice Augmented with LLMs
In recent years, large language models (LLMs) have become more ubiquitous in the workplace. In software engineering, they are often realized as “copilots" which produce code given a prompt or existing code. Programmers using these tools to increase their coding productivity need to be proficient in …
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Large Language Model Routing with Benchmark Datasets
There is a rapidly growing number of open-source Large Language Models (LLMs) and benchmark datasets to compare them. While some models dominate these benchmarks, no single model typically achieves the best accuracy in all tasks and use cases. With a new dataset, it can be difficult to determine …
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