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University of Stavanger

Academic institutioneurope · no
Official website
Research library35linked papers
Opportunities0open roles
Selected work

Representative Papers

Small Is Enough: Per-User Style Rewriting of AI-Edited Text via LoRA Adapters

Jul 31, 2026

This work proposes a privacy-preserving, instruction-free approach to personalized text rewriting that achieves style transfer using only a user’s own documents, eliminating the need for in-context prompting during inference. The method leverages multiple local auxiliary large language models to construct training pairs, fine-tunes base models ranging from 0.5B to 7B parameters via LoRA, and incorporates automatic text chunking with length-aware generation. Evaluation on 219 scientific paper rewriting pairs reveals a performance plateau at an automatic composite score of 0.69, indicating that even small models are sufficiently effective. Human evaluations by LLM judges show an average reduction of over 20% in perceived “AI-ness” of outputs, which further decreases with larger model scales. This study is the first to demonstrate a scaling saturation effect in this task, validating the efficacy and practicality of lightweight, single-user adaptation frameworks.

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Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training

Jun 10, 2026

This work addresses the deployment challenges of existing parameter-efficient fine-tuning methods—such as LoRA and Soft Prompting—which require modifications to the model’s computation graph and are thus incompatible with high-throughput inference engines like vLLM. The authors propose ART, a novel approach that treats visual inputs as learnable “computational art.” By optimizing only pixel-level visual prompts while keeping all parameters of the multimodal large language model frozen, ART injects task-specific information without altering the model architecture or computation graph. Consequently, it seamlessly integrates with precompiled inference systems and supports arbitrary fine-tuning objectives. Experimental results demonstrate that ART achieves performance on par with LoRA on Qwen-series models across multiple textual benchmarks, particularly excelling in mathematical reasoning and structured tool-use tasks.

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Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMs

Jun 07, 2026

This work addresses the challenge of high-throughput, low-latency fact-checking in multilingual settings by proposing a modular system comprising three stages: claim detection, evidence retrieval and re-ranking, and veracity prediction. Instead of relying on general-purpose large language models, the system employs task-specific fine-tuned models—XLM-RoBERTa-Large for claim detection, mmBERT-base for stance classification, and a SetFit-based multilingual re-ranker—achieving robust and efficient performance across 114 languages for claim detection and 28 languages for veracity prediction. Experimental results demonstrate that this approach significantly outperforms mainstream large language models such as GPT-5.2, Claude Opus 4.6, and Qwen3-8B in terms of inference efficiency, data privacy, and resource consumption, while maintaining comparable or superior accuracy.

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Recent publications

Latest Papers

Small Is Enough: Per-User Style Rewriting of AI-Edited Text via LoRA Adapters

Jul 31, 2026

This work proposes a privacy-preserving, instruction-free approach to personalized text rewriting that achieves style transfer using only a user’s own documents, eliminating the need for in-context prompting during inference. The method leverages multiple local auxiliary large language models to construct training pairs, fine-tunes base models ranging from 0.5B to 7B parameters via LoRA, and incorporates automatic text chunking with length-aware generation. Evaluation on 219 scientific paper rewriting pairs reveals a performance plateau at an automatic composite score of 0.69, indicating that even small models are sufficiently effective. Human evaluations by LLM judges show an average reduction of over 20% in perceived “AI-ness” of outputs, which further decreases with larger model scales. This study is the first to demonstrate a scaling saturation effect in this task, validating the efficacy and practicality of lightweight, single-user adaptation frameworks.

0 citationsRead paper

Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training

Jun 10, 2026

This work addresses the deployment challenges of existing parameter-efficient fine-tuning methods—such as LoRA and Soft Prompting—which require modifications to the model’s computation graph and are thus incompatible with high-throughput inference engines like vLLM. The authors propose ART, a novel approach that treats visual inputs as learnable “computational art.” By optimizing only pixel-level visual prompts while keeping all parameters of the multimodal large language model frozen, ART injects task-specific information without altering the model architecture or computation graph. Consequently, it seamlessly integrates with precompiled inference systems and supports arbitrary fine-tuning objectives. Experimental results demonstrate that ART achieves performance on par with LoRA on Qwen-series models across multiple textual benchmarks, particularly excelling in mathematical reasoning and structured tool-use tasks.

0 citationsRead paper

Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMs

Jun 07, 2026

This work addresses the challenge of high-throughput, low-latency fact-checking in multilingual settings by proposing a modular system comprising three stages: claim detection, evidence retrieval and re-ranking, and veracity prediction. Instead of relying on general-purpose large language models, the system employs task-specific fine-tuned models—XLM-RoBERTa-Large for claim detection, mmBERT-base for stance classification, and a SetFit-based multilingual re-ranker—achieving robust and efficient performance across 114 languages for claim detection and 28 languages for veracity prediction. Experimental results demonstrate that this approach significantly outperforms mainstream large language models such as GPT-5.2, Claude Opus 4.6, and Qwen3-8B in terms of inference efficiency, data privacy, and resource consumption, while maintaining comparable or superior accuracy.

0 citationsRead paper