Summary of Reprogramming Foundational Large Language Models(llms) For Enterprise Adoption For Spatio-temporal Forecasting Applications: Unveiling a New Era in Copilot-guided Cross-modal Time Series Representation Learning, by Sakhinana Sagar Srinivas et al.
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning
by Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi, Venkataramana Runkana
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel hybrid approach to spatio-temporal forecasting combines the strengths of open-source language models (LLMs) and large-scale language models (LMs) with traditional forecasting methods. The approach leverages dynamic prompting, grouped-query multi-head attention, and fine-tuning smaller LMs for time series trend analysis. This enables accurate forecasts on consumer-grade hardware using Low-Rank Adaptation with Activation Memory Reduction (LoRA-AMR). The framework outperforms existing methods by significant margins on various real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spatio-temporal forecasting helps predict things like traffic, delivery times, and supply chain movements. Right now, we don’t have great ways to handle really big datasets for this type of forecasting. To fix this, we’re combining different types of language models with traditional forecasting methods. This lets us better capture patterns in time series data and make more accurate predictions. We can even customize this approach to work on smaller computers using special techniques that reduce how much memory is needed. Our tests show that this new approach works really well and outperforms other methods. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Low rank adaptation » Multi head attention » Prompting » Time series
