Summary of Large Language Models For Forecasting and Anomaly Detection: a Systematic Literature Review, by Jing Su et al.
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
by Jing Su, Chufeng Jiang, Xin Jin, Yuxin Qiao, Tingsong Xiao, Hongda Ma, Rong Wei, Zhi Jing, Jiajun Xu, Junhong Lin
First submitted to arxiv on: 15 Feb 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 This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospective future directions. LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains. The review identifies several critical challenges that impede their broader adoption and effectiveness, including issues with generalizability, model hallucinations, limitations within the models’ knowledge boundaries, and computational resource requirements. To overcome these obstacles, potential solutions include integrating multimodal data, advancements in learning methodologies, and emphasizing model explainability and computational efficiency. The review also outlines critical trends shaping the evolution of LLMs, including real-time processing, sustainable modeling practices, and interdisciplinary collaboration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Large Language Models (LLMs) are used to predict things that might happen in the future or detect unusual behavior. They’re really good at finding patterns in big datasets and making predictions. But there are some big challenges with using them, like not being able to work well outside of what they’ve been trained on, making mistakes by predicting things that aren’t real, and needing a lot of computer power. The paper talks about how we can make these models better, such as by combining different types of data or finding new ways for the computers to learn. It also looks at the future of LLMs in this area. |
Keywords
* Artificial intelligence * Anomaly detection * Parsing
