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 1031 for “"LLM"”.
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Understanding LLM Communictaion
The training of modern Large Language Models (LLMs) requires distributed computing across Graphics Processing Unit (GPU) clusters, where network communication efficiency critically impacts performance and cost. Existing profiling tools provide either high-level metrics or low-level timing data, but …
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Simulating LLM Runtime Latency
Large Language Models (LLMs) are expensive to run and can incur high latencies. Each LLM application has its own cost and latency targets. For example, AI voice assistants operate under low latency objectives, while large document batch processing jobs are typically cost-sensitive. However, …
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ResearchBuddy AI: LLM-Powered Assistant
This paper presents ResearchBuddy AI, an LLM-powered assistant that addresses modern research information overload problems. The project has three essential features, including finetuning of large language models for structured research paper summarization, prompt engineering for flexible …
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LEO: an LLM-Powered EDA Overview
Computational notebooks impose a linear structure that impedes data analysts’ sensemaking process with overwritten cells, dead-end code, and fragmented logic. This challenge is especially pronounced when analysts either encounter a notebook authored by someone else or revisit a self-authored …
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LLM-Directed Agent Models in Cyberspace
… the application of Large Language Models (LLMs) for automating penetration tests and Cyber Capture the Flag (CTF) challenges, bridging the gap between static tools and dynamic human intuition in cybersecurity. This work provides an evaluation framework for assessing the performance of LLMs …
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Bypassing LLM watermarks with color-aware substitutions
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01
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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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LLM-Supported Natural Language to Bash Translation
… capabilities of large language models (LLMs) for command composition alleviates these issues. However, the NL2SH performance of LLMs is difficult to assess due to inaccurate test data and unreliable heuristics for determining the functional equivalence of Bash commands. We present a …
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Addressing data challenges in LLM-enhanced software engineering
The rapid adoption of large language models (LLMs) is reshaping software engineering practice, yet it reveals a critical dichotomy: while resource-intensive tasks like code generation benefit from large datasets and standardized benchmarks, they also face significant risks from data contamination …
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Efficient LLM training and inference with contextual sparsity
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01
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Initiation and Growth of Reaction in LLM-105
… this thesis was to understand the behaviour of LLM-105, a new explosive material, when subjected to impact. Explosive reactivity under impact can be thought of in terms of safety (sensitiveness) and response to shock loading (sensitivity). The experimental work presented addresses both regimes, …
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Impacts of LLM-Based Text Normalization on Price Prediction
This study investigates the effect of LLM-based text normalization on price prediction from user-generated product descriptions. Using the Mercari Price Suggestion Challenge dataset, we normalize 120,000 item descriptions with GPT-4o-mini and evaluate the impact across three modeling pipelines: a …
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Automatically Improving The Code Quality Of Rust Via LLM
… define and resolve the challenges of leveraging LLM to automatically improve Rust’s code quality. The application of LLMs to Rust code quality improvement requires addressing fundamental challenges in three key areas: generating compilable code that satisfies Rust’s strict type system, detecting …
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LLM-powered active learning for cost-effective text classification
This thesis presents an LLM-powered active learning framework for cost-effective text classification, addressing the challenge of potential LLM annotation errors while balancing annotation quality and model accuracy. Our methodology combines human and large language model (LLM) annotations using …
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Taxonomie pro LLM v komponentě Kafka projektu Apache Camel
Tato práce se zaměřuje na koncept umělé inteligence, konkrétně na velké jazykové modely (Large Language Models). Popisuje základní principy neuronových sítí a fáze trénování v oblasti umělé inteligence, strojového učení a jazykových modelů, přičemž se zabývá i výzvami efektivního trénování …
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SLO-aware optimization and stateful orchestration for LLM systems
The rapid evolution of Large Language Models (LLMs) has shifted the focus of AI infrastructure from simple text generation to complex, multi-turn agentic workflows. As these applications become increasingly sensitive to latency and dependencies, existing serving systems—which primarily optimize for …
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Improving Accuracy Predictions of Companion Classifiers for LLM Routing
… 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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Can an LLM find its way around a Spreadsheet?
… highly specialized pipelines. We ask whether an LLM can find its way around a spreadsheet and how to support end-users in taking their free-form data processing requests to fruition. Just like RAG retrieves context to answer users' queries, we demonstrate how we can retrieve elements from a code …
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CLOSED-LOOP SCALING: AUTONOMOUS IMPROVEMENT OF LLM AND LVLM REASONING
… 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. Inference-time scaling …
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Consistency-aware and LLM-assisted methods for named entity recognition
… thesis investigates how large language models (LLMs) can be leveraged to enhance NER and relation extraction in data-scarce clinical settings. Although LLMs are capable of generating diverse and contextually rich text, their direct application to clinical information extraction poses challenges …
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