Summary of Calf: Aligning Llms For Time Series Forecasting Via Cross-modal Fine-tuning, by Peiyuan Liu et al.
CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning
by Peiyuan Liu, Hang Guo, Tao Dai, Naiqi Li, Jigang Bao, Xudong Ren, Yong Jiang, Shu-Tao Xia
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep learning models, specifically Transformers, have been highly effective in predicting future values in multivariate time series forecasting. Recent advancements in large language model-based methods have shown significant improvements when using both text and time series inputs. However, these methods often neglect the differences in distribution between textual and temporal input data, resulting in suboptimal performance. To address this issue, researchers propose a novel Cross-Modal LLM Fine-Tuning (CALF) framework that reduces this distribution discrepancy by aligning cross-modal input distributions using a developed cross-modal match module. Additionally, feature regularization loss is introduced to align intermediate features between branches for better weight updates, and output consistency loss ensures the output representations of both branches correspond effectively. CALF achieves state-of-the-art performance in long-term and short-term forecasting tasks with low computational complexity and favorable few-shot and zero-shot abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multivariate time series forecasting is a way to predict future values based on past data. Researchers used special kinds of artificial intelligence called Transformers to make these predictions. They tried using both text and time series data, which worked better than just using one type of data. However, they noticed that the text and time series data were different in some ways, which made it harder to get good results. To fix this problem, they created a new way to fine-tune their models called CALF. This method helps align the text and time series data so that the model can make better predictions. It works really well and is faster than other methods. |
Keywords
* Artificial intelligence * Deep learning * Few shot * Fine tuning * Large language model * Regularization * Time series * Zero shot
