用大模型操控社交媒体情绪，提升智能交易盈利
Exploring Sentiment Manipulation by LLM-Enabled Intelligent Trading Agents
- 用连续深度强化学习控制大模型发布社交内容
- 能通过调节发帖情绪提升自身利润
- 适合关注AI金融操控与市场伦理的研究者
各大经济领域企业正快速部署大型语言模型。由于其与基于人类反馈微调语言模型的关联，强化学习再次受到关注。工具链语言模型可控制特定任务的智能体；反向情形不久也将出现。本文首次研究基于连续深度强化学习的智能交易代理，该代理同时控制一个大型语言模型，可向其他交易者可见的社交媒体平台发布内容。我们在模拟金融市场中实证检验了该代理的性能与影响，发现其能够通过优化自身总奖励，从而提升利润，方法是操纵其生成帖子的情感倾向。论文最后讨论了研究局限性，并提出未来工作方向。
原文摘要 · Abstract (English)
Companies across all economic sectors continue to deploy large language models at a rapid pace. Reinforcement learning is experiencing a resurgence of interest due to its association with the fine-tuning of language models from human feedback. Tool-chain language models control task-specific agents; if the converse has not already appeared, it soon will. In this paper, we present what we believe is the first investigation of an intelligent trading agent based on continuous deep reinforcement learning that also controls a large language model with which it can post to a social media feed observed by other traders. We empirically investigate the performance and impact of such an agent in a simulated financial market, finding that it learns to optimize its total reward, and thereby augment its profit, by manipulating the sentiment of the posts it produces. The paper concludes with discussion, limitations, and suggestions for future work.
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