Summary of Adversarial Vulnerabilities in Large Language Models For Time Series Forecasting, by Fuqiang Liu et al.
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
by Fuqiang Liu, Sicong Jiang, Luis Miranda-Moreno, Seongjin Choi, Lijun Sun
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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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 paper introduces a targeted adversarial attack framework for Large Language Models (LLMs) in time series forecasting. LLMs have shown impressive capabilities in handling complex temporal data, but their robustness and reliability remain under-explored, particularly concerning their susceptibility to attacks. The authors employ gradient-free and black-box optimization methods to generate minimal yet effective perturbations that significantly degrade forecasting accuracy across multiple datasets and LLM architectures. Experiments with models like LLMTime, GPT-3.5, GPT-4, LLaMa, Mistral, TimeGPT, and TimeLLM demonstrate the broad effectiveness of attacks, highlighting vulnerabilities and the need for robust defense mechanisms. The results underscore the importance of ensuring reliable deployment in practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to trick Large Language Models (LLMs) used for predicting future events, like stock prices or weather. LLMs are good at this task, but they can be easily fooled by fake data. The researchers created a special kind of fake data that is hard for the LLMs to detect. They tested their method on several different types of LLMs and found that it worked well across all of them. This means that we need to find ways to protect our predictions from these kinds of attacks. |
Keywords
» Artificial intelligence » Gpt » Llama » Optimization » Time series
