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.
Results
Showing 1 to 20 of 34 for “"finetuning"”.
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Automated Finetuning via Sparse Autoencoders
… performance in small models via instruction finetuning. Specifically, we present UnderstandTune, an autonomous method for assembling high-quality instruction finetuning datasets with minimal human intervention, requiring only concise task descriptions rather than evaluation dataset …
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Comparing Parameter Efficient Finetuning Techniques (PEFT) using Datamodels
… and costly, especially due to the extensive finetuning required. Parameter-efficient finetuning techniques (PEFT) have been proposed to address this issue by significantly reducing the number of trainable parameters, achieving comparable results to full-parameter finetuning. Despite …
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Learning to Interpret Language Model Diffs
Finetuning-induced changes to a model’s weights (a “model diff”) are semantically meaningful but often difficult to interpret. This makes us wonder: can we describe the content of an unknown model diff using natural language? We introduce diff interpretation training, a method that teaches a model …
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Steerable Alignment with Conditional Multiobjective Preference Optimization
… (RLHF) have provided useful paradigms for finetuning LLMs to produce outputs that are more consistent with human preferences. These approaches, however, assume that preferences are formed by a single, underlying reward model, which is likely insufficient for representing an individual’s …
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Neural Network Pruning for ECG Arrhythmia Classification
… employs a pruning phase interleaved with a finetuning phase. It is shown that when performing the scale-factor pruning algorithm on ECG, finetuning time can be expedited by 1.4 times over the traditional approach with only 10% of expensive floating-point operations retained, while …
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Relation extraction: exploring syntax parsing and constructing it as attention-like structure
… tree structure to matrixes and apply on the finetuning process of SyntaxBERT [2] and syntax-aware -local-attention attention BERT (SLA) [3] to strengthen their ability to learning entity relations. They would be finetuned on TACRED [4] dataset. They are compared with the finetuning of …
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Data-Efficient Learning in Image Synthesis and Instance Segmentation
… high-quality and diverse image generation from finetuning to only 5-100 images. Our method factors a pretrained model into a small but highly expressive weight space for finetuning, which discourages overfitting in a small training set. We validate our method in a challenging few-shot setting of …
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Inference Time Search for Protein Structure Prediction
… search by adding architectural components and a finetuning procedure to state-of-the-art structure prediction models that give rise to a discrete latent space. We implement algorithms for searching and sampling in this discrete latent space and conduct experiments on a small model, demonstrating …
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Understanding the Robustness of Vision Models and Humans to Occlusion-Based Corruptions
… can be mitigated through two approaches: finetuning using occluded images and inpainting occluded pixels before classification. We discover that finetuning leads to a considerable increase in accuracy, but we suspect that finetuned models are relying on a different set of features. …
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Adapting Transformers for Structured Data Domains
… present RAFT-S3, a framework for reasoning-aware finetuning of small language models (SLMs) on the text-to-SQL task. RAFT-S3 collects synthetic text-to-SQL data with diverse schemas using large language models (LLMs), along with intermediate reasoning traces which are incorporated into the …
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First-Principles Study of Electronic Structure and Optical Properties of Semiconductor Surfaces Unified Approach for Exact Calculation of Coupling Coefficients of Quantum Angular Momenta
… can be stabilized in these systems without much finetuning of external conditions. Our numerical results are interpreted in light of non-adiabatic rate theory.
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ScPEFT : a parameter-efficient fine-tuning framework for enhancing single-cell large language models in out-of-context
… outperformed zero-shot models and traditional finetuning in disease-specific, cross-species, and under-characterized cell population tasks. Its attention-mechanism analysis identified COVID-related genes associated with specific cell states and uncovered unique blood cell subpopulations, …
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Evaluating Data Augmentation with Attention Masks for Context Aware Transformations
… augmentations which can be used for further finetuning. Our comprehensive analysis points to limited success of utilizing this context-aware augmentation method. By shedding light on its strengths and limitations, we offer insights that can guide the selection of optimal augmentation …
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Steering Robots with Inference-Time Interactions
… behavior. While collecting additional data for finetuning can address such issues, doing so for each downstream use case is inefficient at deployment. My research proposes an alternative: keeping pretrained policies frozen as a fixed skill repertoire while allowing user interactions to guide …
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Enhanced Potts Models for Improved Computational Protein Design
… loss function to successfully perform finetuning to improve TERMinator’s performance on orthogonal energetic benchmarks. Finally, I detail an observed disconnect between accuracy on energetic benchmarks and native sequence recovery, illustrating the deficiency of only using native …
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Mixed-Variable Bayesian Optimization using Prior-Data Fitted Networks
… optimization. Additionally, we explore how finetuning PFNs on targeted function priors can enhance performance when prior knowledge about the objective is available. Our contributions include empirical evaluations of mixed-BO techniques, insights into PFN training, and a suite of …
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ResearchBuddy AI: LLM-Powered Assistant
… project has three essential features, including finetuning of large language models for structured research paper summarization, prompt engineering for flexible summarization of variable formats of GitHub READMEs, and Retrieval-Augmented Generation (RAG) for context-aware question answering over …
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Embodied multimodal referring expressions generation
… to generate more data for model training; 5) Finetuning LLaMA 2-chat-13B for generating contextually-correct and situationally-fluent multimodal referring expressions; 6) Integrating the fine-tuned model into the IVA to evaluate the success of the generative model-enabled IVA in communication …
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Building a Language Conditioned System for 6-DoF Tabletop Manipulation
… to either train an end-to-end model or for finetuning. Further, the recent advancements in large models such as Segment Anything and GPT-4 made it possible to construct a modular system, that incorporates vast common sense knowledge, as opposed to traditional approaches. These large models …
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Emergent Capabilities of Generative Models: “Software 3.0” and Beyond
… programmers are tasked with orchestrating and finetuning the interactions between large-scale foundation models to carry out higher-order tasks.
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