Recently, large language models (LLMs) with conversational-style interaction, such as ChatGPT and Claude, have gained significant importance in the advancement of artificial general intelligence (AGI). However, the extensive resource requirements during pre-training, instruction fine-tuning (IF), and reinforcement learning through human feedback (RLHF) pose challenges, particularly for individuals and studios with limited resources. Moreover, sensitive data that cannot be deployed on remote training platforms or queried through APIs further exacerbates this issue. To address these limitations, researchers have introduced a parameter-efficient framework called low-rank adaptation (LoRA) for IF on LLMs. However, training individual LoRA networks faces capacity constraints and struggles to adapt to large domains with significant distributional shifts across different tasks. In this paper, we propose a novel framework called chain-of-LoRA to enhance the IF performance of LoRA. Our approach involves training a LoRA network to classify the instruction type and then utilizing task-specific LoRA networks to accomplish the respective tasks. By training multiple task-specific LoRA networks, we exploit a trade-off between performance and disk storage, leveraging the easily expandable and cost-effective nature of disk storage compared to precious graphical resources. Our experimental results demonstrate that our proposed framework achieves comparable performance to typical direct IF on LLMs.

In the field of time series forecasting, time series are often considered as linear time-varying systems, which facilitates the analysis and modeling of time series from a structural state perspective. Due to the non-stationary nature and noise interference in real-world data, existing models struggle to predict long-term time series effectively. To address this issue, we propose a novel model that integrates the Kalman filter with a state space model (SSM) approach to enhance the accuracy of long-term time series forecasting. The Kalman filter requires recursive computation, whereas the SSM approach reformulates the Kalman filtering process into a convolutional form, simplifying training and enhancing model efficiency. Our Kalman-SSM model estimates the future state of dynamic systems for forecasting by utilizing a series of time series data containing noise. In real-world datasets, the Kalman-SSM has demonstrated competitive performance and satisfactory efficiency in comparison to state-of-the-art (SOTA) models.