Data Preparation (DP), which involves tasks such as data cleaning, imputation and integration, is a fundamental process in data-driven applications. Recently, Large Language Models (LLMs) fine-tuned for DP tasks, i.e., DP-LLMs, have achieved state-of-the-art performance. However, transferring DP-LLMs to novel datasets and tasks typically requires a substantial amount of labeled data, which is impractical in many real-world scenarios. To address this, we propose a knowledge augmentation framework for data preparation, dubbed KNOWTRANS. This framework allows DP-LLMs to be transferred to novel datasets and tasks with a few data points, significantly decreasing the dependence on extensive labeled data. KNOWTRANS comprises two components: Selective Knowledge Concentration and Automatic Knowledge Bridging. The first component re-uses knowledge from previously learned tasks, while the second automatically integrates additional knowledge from external sources. Extensive experiments on 13 datasets demonstrate the effectiveness of KNOWTRANS. KNOWTRANS boosts the performance of the state-of-the-art DP-LLM, Jellyfish-7B, by an average of 4.93%, enabling it to outperform both GPT-4 and GPT-4o.

Abstract Low-Rank Adaptation (LoRA), which updates the dense neural network layers with pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning paradigms. Furthermore, it has significant advantages in cross-task generalization and privacy-preserving. Hence, LoRA has gained much attention recently, and the number of related literature demonstrates exponential growth. It is necessary to conduct a comprehensive overview of the current progress on LoRA. This survey categorizes and reviews the progress from the perspectives of (1) downstream adaptation improving variants that improve LoRA’s performance on downstream tasks; (2) cross-task generalization methods that mix multiple LoRA plugins to achieve cross-task generalization; (3) efficiency-improving methods that boost the computation-efficiency of LoRA; (4) data privacy-preserving methods that use LoRA in federated learning; (5) application. Besides, this survey also discusses the future directions in this field.