Generative Parameter-Efficient Fine-Tuning
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GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models
arXiv:2506.11042v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific updates $\Delta W$. Existing methods often learn $\Delta W$ largely independently of pretrained weights $W_0$, or exploit $W_0$ mainly through initialization or simple reparameterization. To further leverage the structural information encoded in $W_0$, we propose...
Parameter-Efficient Multi-Task Fine-Tuning in Code-Related Tasks
Announce Type: replace Abstract: Large Language Models (LLMs) have proven highly effective in automating software engineering tasks, bridging natural language and code semantics to achieve notable results in code generation and summarization. However, their scale incurs substantial computational costs, making full fine-tuning impractical. Parameter-Efficient Fine-Tuning (PEFT) methods like QLoRA enable efficient specialization with lower resource demands.
Adapting Large Language Models to a Low-Resource Agglutinative Language: A Comparative Study of LoRA and QLoRA for Bashkir
Announce Type: replace Abstract: This paper presents a comparative study of parameter-efficient fine-tuning (PEFT) methods, including LoRA and QLoRA, applied to the task of adapting large language models to the Bashkir language, a low-resource agglutinative language of the Turkic family. Experimental evaluation is conducted on a Bashkir text corpus of 71k documents (46.9M tokens) using models of various architectures: DistilGPT2, GPT-2 (base, medium), Phi-2, Qwen2.5-7B, DeepSeek-7B, and...
GoodVibe: Security-by-Vibe for LLM-Based Code Generation
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ImmigrationQA: A Source-Grounded Dataset and Small-Model Adaptation for U.S. Immigration Law
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Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution
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EURO-5K: When Does Domain Pretraining Matter? Benchmarking Transformers for EU Reporting Obligation Extraction
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EvoPrompt: Guided Prompt Evolution for Vision-Language Models Adaptation
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Assessment of Generative Named Entity Recognition in the Era of Large Language Models
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PEFT of SLM for Telecommunications Customer Support: A Comparative Study of LoRA Configurations with Energy Consumption Analysis
Announce Type: new Abstract: While large language models (LLMs) show strong performance in natural language understanding and generation, their evaluation and adaptation to domain-specific constraints in telecommunications customer support remain limited. In addition, data sovereignty, regulatory constraints, and the handling of sensitive customer and network information complicate the use of externally hosted foundation models in this domain. We present a systematic study of...