Summary of Towards Time Series Reasoning with Llms, by Winnie Chow et al.
Towards Time Series Reasoning with LLMs
by Winnie Chow, Lauren Gardiner, Haraldur T. Hallgrímsson, Maxwell A. Xu, Shirley You Ren
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes a novel multi-modal approach to large language models (LLMs) for time-series reasoning in natural language. The authors train a lightweight time-series encoder on top of an LLM, allowing the model to directly extract time-series information. They then fine-tune their model with chain-of-thought augmented time-series tasks to encourage the model to generate reasoning paths. The proposed approach shows promising performance in zero-shot reasoning tasks across various domains, including outperforming GPT-4o on a set of tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using big language models to understand and reason about time-series data, like stock prices or weather patterns. Usually, these models are great at understanding images or text, but not time-series data. The authors come up with a new way to train a model that can learn from time-series data and use it for reasoning tasks. They show that their model can do this better than another popular model called GPT-4o. |
Keywords
» Artificial intelligence » Encoder » Gpt » Multi modal » Time series » Zero shot
