Parameter-efficient finetuning (PEFT) is a key technique for adapting large
language models (LLMs) to downstream tasks. In this paper, we study leveraging
knowledge graph embeddings to improve the effectiveness of PEFT. We propose a
knowledgeable adaptation method called KnowLA. It inserts an adaptation layer
into an LLM to integrate the embeddings of entities appearing in the input
text. The adaptation layer is trained in combination with LoRA on instruction
data. Experiments on six benchmarks with two popular LLMs and three knowledge
graphs demonstrate the effectiveness and robustness of KnowLA. We show that
\modelname can help activate the relevant parameterized knowledge in an LLM to
answer a question without changing its parameters or input prompts.

通过将知识图嵌入到大型语言模型中，我们提出了一种名为 KnowLA 的知识适应方法，该方法通过在语言模型中插入适应层，将文本中出现的实体的嵌入融入到语言模型中，并与 LoRA 一起进行训练。实验结果表明 KnowLA 的有效性和鲁棒性，能够在不改变语言模型参数或输入提示的情况下，激活语言模型中的相关参数化知识以回答问题。

KnowLA: 提升参数高效微调与专业适应性

KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable  Adaptation

Recent reports suggest that a generic supervised deep CNN model trained on a
large-scale dataset reduces, but does not remove, dataset bias on a standard
benchmark. Fine-tuning deep models in a new domain can require a significant
amount of data, which for many applications is simply not available. We propose
a new CNN architecture which introduces an adaptation layer and an additional
domain confusion loss, to learn a representation that is both semantically
meaningful and domain invariant. We additionally show that a domain confusion
metric can be used for model selection to determine the dimension of an
adaptation layer and the best position for the layer in the CNN architecture.
Our proposed adaptation method offers empirical performance which exceeds
previously published results on a standard benchmark visual domain adaptation
task.

本论文提出了一种基于深度卷积神经网络的适应性层及额外的领域混淆损失函数，学习既有语义意义且兼具领域不变性的表示。此方法提供了优异的实验性能，超过了之前已发表成果，如在标准化基准视觉领域适应任务的结果。