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<p>The digital transformation of public services has created an urgent demand for intelligent question-answering (QA) systems that can deliver both accurate and context-aware responses. While Low-Rank Adaptation (LoRA) enables efficient fine-tuning of large language models (LLMs) for domain-specific tasks, it struggles to capture sparse yet critical personalized information–such as residency status, age, or contribution history–that appears in only a small fraction of training data. This paper proposes SimLoRA, a novel parameter-efficient fine-tuning method that integrates the SimAM (A Simple, Parameter-Free Attention Module) 3D attention mechanism into the LoRA framework. SimLoRA dynamically weights low-rank updates based on neuronal distinctiveness, amplifying the contribution of informative features while suppressing generic patterns, all without introducing additional trainable parameters. We evaluate our approach on a real-world citizen card QA dataset from Hangzhou, China, consisting of 70,000 QA pairs where 3% of queries contain personalized information. Furthermore, as an optional enhancement, applying Reinforcement Learning from Human Feedback (RLHF) using the Proximal Policy Optimization (PPO) algorithm yields a 57.2% win rate against the SimLoRA baseline in real-world preference-based evaluation, producing responses better aligned with user expectations for public service scenarios. SimLoRA offers an effective, computationally efficient pathway for deploying personalized LLM-based QA systems in critical public service domains where accuracy and personalization are paramount.</p>

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SimLoRA for enhanced domain specific LLM fine tuning using attention weighted low rank adaptation and RLHF

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月30日

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  • Wujian Yang,
  • Huibao Zhang,
  • Haotian Jin,
  • Sunyang Chen,
  • Qihao Shi,
  • Guanlin Chen

摘要

The digital transformation of public services has created an urgent demand for intelligent question-answering (QA) systems that can deliver both accurate and context-aware responses. While Low-Rank Adaptation (LoRA) enables efficient fine-tuning of large language models (LLMs) for domain-specific tasks, it struggles to capture sparse yet critical personalized information–such as residency status, age, or contribution history–that appears in only a small fraction of training data. This paper proposes SimLoRA, a novel parameter-efficient fine-tuning method that integrates the SimAM (A Simple, Parameter-Free Attention Module) 3D attention mechanism into the LoRA framework. SimLoRA dynamically weights low-rank updates based on neuronal distinctiveness, amplifying the contribution of informative features while suppressing generic patterns, all without introducing additional trainable parameters. We evaluate our approach on a real-world citizen card QA dataset from Hangzhou, China, consisting of 70,000 QA pairs where 3% of queries contain personalized information. Furthermore, as an optional enhancement, applying Reinforcement Learning from Human Feedback (RLHF) using the Proximal Policy Optimization (PPO) algorithm yields a 57.2% win rate against the SimLoRA baseline in real-world preference-based evaluation, producing responses better aligned with user expectations for public service scenarios. SimLoRA offers an effective, computationally efficient pathway for deploying personalized LLM-based QA systems in critical public service domains where accuracy and personalization are paramount.