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 341 for “"LLMs"”.
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Examining LLMs in Economic Settings
… in text data on which large language models (LLMs) are trained, to what extent are LLMs prone to the same behavioral biases? Understanding these biases in LLMs is crucial for deploying LLMs to support human decision-making. To enable the responsible deployment of LLMs, I propose economic …
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Hosting LLMs on Shared GPUs
Large language models (LLMs) have emerged as powerful tools for a wide array of applications. Serving multiple LLMs on shared GPUs has increasingly gained attention as single providers need to support multiple applications (summarization, chat, code generation), different model versions (A/B …
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SWE-Bench+: Enhanced Coding Benchmark for LLMs
Large Language Models (LLMs) in Software Engineering (SE) can offer valuable assistance for coding tasks. To facilitate a rigorous evaluation of LLMs in practical coding contexts, Carlos et al. introduced the SWE-bench dataset, which comprises 2,294 real-world GitHub issues. Several impressive …
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Detection and mitigation of misbehaviour in LLMs
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Entity-based long document summarization using LLMs
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
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TIGER: Testing and Improving Generated Code with LLMs
… of code generated by Large Language Models (LLMs), focusing on real-world applicability and minimal developer assistance. The system is designed to simulate a realistic development environment where no ground-truth implementations are available to the model, relying exclusively on textual …
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Adversarial Prompt Transformation for Systematic Jailbreaks of LLMs
The rapid integration of Large Language Models (LLMs) like OpenAI’s GPT series into diverse sectors has significantly enhanced digital interactions but also introduced new security challenges, notably the risk of "jailbreaking" where inputs cause models to deviate from their operational guidelines. …
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Multi-Agent LLMs for Adaptive Acquisition in Bayesian Optimization
Bayesian Optimization (BO) is widely used to optimize expensive black-box objectives by learning a probabilistic surrogate and querying new points via an acquisition function. Classic approaches, however, bind acquisition behavior tightly to the surrogate (often a GP) and a small set of fixed …
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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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Automated Care Pathway Modeling Using Agentic and Knowledge-Aware LLMs
… modeling using large language models (LLMs). We compare two contemporary frameworks - MAO (agentic, multi-role orchestration) and ProMoAI (single-agent with self-refinement loop) - under controlled execution with standardized evaluation. Automated metrics combine node-level and …
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MOBLLM: Model Building LLMs via Symbolic Regression and Experimental Design
Large language models (LLMs) have recently emerged for daily use and have already been extensively utilized for various tasks. They are shown to be able to carry out more and more complex tasks every day, including those that require a high level of formal/mathematical reasoning at human or …
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Toward Deliberative AI: Multi-Agent LLMs for Real-World Reasoning
… reasoning abilities of large language models (LLMs). However, existing approaches often fall short due to inefficiencies, shallow agreement, and a lack of real-world applicability. In this thesis, we introduce two novel frameworks CONSENSAGENT and CCAGENTdesigned to improve both the …
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Can LLMs implicitly learn numeric parameter constraints in data science APIs?
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01
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An Empirical Evaluation of LLMs for the Assessment of Subjective Qualities
Large Language Models (LLMs) have achieved remarkable success in natural language processing tasks and are increasingly being used for language generation. Significant advancements in this field have unlocked capabilities that enable their adoption in sophisticated roles, including acting as …
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CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs
The role of Large Language Models (LLMs) has not been extensively explored in analog circuit design, which could benefit from a reasoning-based approach that transcends traditional optimization techniques. In particular, despite their growing relevance, there are no benchmarks to assess LLMs’ …
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Conversational Multimodal LLMs for Food Nutritional Information Retrieval: A Systematic Evaluation
Accurate dietary monitoring underpins public health initiatives, chronic disease management, and personalized nutrition, yet manual food logging remains laborious and prone to error. At the same time, vision and language models have reached new levels of capability and offer the potential to infer …
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From Form to Meaning: Interlingual Sense-Alignment of Offensive Language with LLMs
Η παρούσα διπλωματική εργασία εκκινεί από τη διαπίστωση ότι η μετάφραση προσβλητικών όρων δυσχεραίνεται από τη μεγάλη ιδιωματικότητα της προσβλητικής γλώσσας. Με γνώμονα αυτό το ζητούμενο, η εργασία διερευνά την ευθυγράμμιση προσβλητικών λεξικών σε επίπεδο σημασίας με τη χρήση μεγάλων γλωσσικών …
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Glass onion: Compositional text-to-image generation using diffusion models and LLMs
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
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Towards transparent representations: on internal structure and external world modeling in LLMs
Large language models (LLMs) generalize far beyond their training distribution, enabling impressive downstream performance in domains vastly different from their pretraining distribution. In this thesis, we develop a data-centric view on machine learning. We suggest that the deep generalization of …
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