Summary of Trading Devil Rl: Backdoor Attack Via Stock Market, Bayesian Optimization and Reinforcement Learning, by Orson Mengara
Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning
by Orson Mengara
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Computational Physics (physics.comp-ph); Physics and Society (physics.soc-ph)
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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 backdoor attack focused on data poisoning in large language models utilizing reinforcement learning is proposed. The FinanceLLMsBackRL attack targets scenarios where well-known financial institutions simulate various models for research teams and operational use. This study examines the potential effects of large language models that employ reinforcement learning systems for text production, speech recognition, or finance applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models using reinforcement learning can be vulnerable to data poisoning attacks, which can have significant implications for everyday applications like finance. Researchers proposed a new type of backdoor attack called FinanceLLMsBackRL that targets large language models without prior consideration or triggers. This study explores the effects of such attacks on text production, speech recognition, and other AI models. |
Keywords
» Artificial intelligence » Reinforcement learning
