研究人员开发了NeuFS，一个新颖的神经元感知主动少样本学习框架，旨在增强大型语言模型（LLMs）在专业领域的适应性。与依赖输出级别信号的现有方法不同，NeuFS利用内部神经元激活模式来识别最有价值的未标记样本进行标注。该方法通过量化神经元共识，确保少样本样本的多样性，并优先处理LLMs容易产生幻觉的挑战性示例。实验表明，NeuFS在推理和文本分类任务中优于当前的主动少样本学习基线，证明了内部神经元激活比外部嵌入更有效。 AI
影响 通过提高少样本学习效率和降低标注成本，增强LLM在专业领域的适应性。
排序理由 详细介绍LLM新适应方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv:2607.02423v1 Announce Type: cross Abstract: Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while promoting h…
Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while promoting high performance. However, existing methods typical…