Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations (2301.04246v1)

Abstract: Generative LLMs have improved drastically, and can now produce realistic text outputs that are difficult to distinguish from human-written content. For malicious actors, these LLMs bring the promise of automating the creation of convincing and misleading text for use in influence operations. This report assesses how LLMs might change influence operations in the future, and what steps can be taken to mitigate this threat. We lay out possible changes to the actors, behaviors, and content of online influence operations, and provide a framework for stages of the LLM-to-influence operations pipeline that mitigations could target (model construction, model access, content dissemination, and belief formation). While no reasonable mitigation can be expected to fully prevent the threat of AI-enabled influence operations, a combination of multiple mitigations may make an important difference.

Overview of Generative LLMs and Influence Operations

The paper "Generative LLMs and Automated Influence Operations: Emerging Threats and Potential Mitigations" thoroughly examines the evolving landscape of influence operations facilitated by generative LLMs. The authors, representing institutions such as Georgetown University's Center for Security and Emerging Technology and OpenAI, explore the ramifications of deploying artificial intelligence systems in shaping public perceptions and the associated social, political, and technological challenges.

Generative LLMs have advanced rapidly in recent years, with demonstrated capabilities in producing coherent and original text. These developments offer significant potential in fields such as healthcare and law. However, the paper centers on the increasing prospects of these models being appropriated for influence operations by malevolent actors aiming to disseminate propaganda.

Potential Impact on Influence Operations

The report identifies how LLMs potentially alter the dynamics of influence operations through changes in actors, behaviors, and content. By reducing the costs and barriers associated with content creation, the models could democratize access to propaganda tools, enabling a broader array of actors to partake in influence operations. Furthermore, the automation of content generation could exponentially increase the scale and scope of these campaigns.

In terms of tactics, LLMs could facilitate the emergence of real-time, interactive propaganda dissemination methods, such as personalized chat interactions. The models promise enhanced linguistic and cultural tailoring, which could improve the persuasive power of generated content. Additionally, the linguistic variability achievable with AI-generated text can make campaigns less detectable by current defensive measures that identify repetitive ‘copypasta’ text patterns.

Challenges and Critical Unknowns

The paper highlights major uncertainties regarding how LLMs might develop further, including enhancements in usability, reliability, and efficiency. These aspects remain significant barriers to the seamless integration of generative text models into influence operations but are expected to diminish over time.

Moreover, the paper speculates on emergent capabilities that may arise as a byproduct of technological scaling and research aimed at general applications. The evolution of generative models could inadvertently yield features that expand their utility in influence operations.

Mitigation Strategies

Responding to these developments requires a multi-faceted approach integrating technological, regulatory, and collaborative efforts. The proposed strategies range from the design of models that inherently produce detectable or fact-sensitive outputs to regulatory actions on data collection and computing resources necessary for training such models.

AI providers could develop access restrictions and cultivate norms against misuse, thereby limiting the deployment of AI for propaganda purposes. For effective content dissemination management, collaboration between platforms and AI developers might help trace and label AI-generated content.

Considerations and Future Directions

The implications of AI-powered propaganda underscore the need for robust mitigations addressing both the supply and demand for misinformation. HaLLMarks of these efforts would include increased media literacy and AI-enabled tools aiding critical consumption of information. Importantly, the report stresses that no singular solution exists to neutralize AI-driven disinformation but rather advocates for a comprehensive societal approach blending legal, commercial, and educational initiatives.

The paper emphasizes the critical role of interdisciplinary research in confronting these challenges, as technical, ethical, and political dimensions converge in the discourse on AI and influence operations. As a result, stakeholders are urged to foster novel collaborations and reinforce normative boundaries that discourage the exploitation of LLMs for manipulative purposes.