Summary of Dualtime: a Dual-adapter Multimodal Language Model For Time Series Representation, by Weiqi Zhang et al.
DualTime: A Dual-Adapter Multimodal Language Model for Time Series Representation
by Weiqi Zhang, Jiexia Ye, Ziyue Li, Jia Li, Fugee Tsung
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent advancements in language models (LMs) have sparked interest in time series modeling, particularly multimodal approaches. However, current methods are biased towards one modality over others, neglecting their complementary benefits. For instance, relying solely on textual reports for seizure diagnosis is insufficient without considering EEG data. This study proposes DualTime, a novel multimodal language model that integrates temporal and textual primary modeling simultaneously. By injecting adaptation tokens, the pipeline achieves efficient fine-tuning and encourages embedding alignment. Experimental results demonstrate the outperformance of state-of-the-art models in both supervised and unsupervised settings, highlighting the complementary benefits of different modalities. Few-shot label transfer experiments further verify the model’s expressiveness and transferability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about using language models to help with time series data analysis, which involves looking at patterns over time. Currently, most methods focus on one type of data, like text or images, without considering how they work together. This can be a problem, like when trying to diagnose seizures from brain wave recordings and medical reports. To solve this, the researchers created a new language model called DualTime that looks at both temporal and textual patterns simultaneously. The results show that this approach is better than previous methods, which is important because it could help with many real-world applications. |
Keywords
» Artificial intelligence » Alignment » Embedding » Few shot » Fine tuning » Language model » Supervised » Time series » Transferability » Unsupervised
