arXiv:2606.03096v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly integrated into various domains, making knowledge editing techniques crucial yet potentially hazardous. Current editing methods primarily target atomic facts, overlooking the significant risks associated with manipulating factual opinions, e.g., documented stances of public figures on societal issues. Such manipulation could reshape public images, influence elections, and alter societal views. To systematically assess this threat, we introduce the Factual Opinion Editing with Evidence (FOE) benchmark,
The increasing integration of LLMs into critical domains necessitates a deeper understanding and control over their knowledge, particularly concerning sensitive factual opinions.
The ability to manipulate factual opinions in LLMs poses significant risks, including the potential to reshape public images, influence elections, and alter societal views, making it a critical area for governance and security.
The introduction of the FOE benchmark provides a systematic method to assess and potentially mitigate the threat of factual opinion manipulation in LLMs, shifting the focus beyond atomic facts to more complex subjective information.
- · AI ethics researchers
- · Democratic institutions
- · Content integrity platforms
- · Disinformation campaigns
- · Unregulated AI developers
- · Public figures vulnerable to manipulation
The FOE benchmark will drive new research and development into robust knowledge editing techniques and defensive measures for LLMs.
Increased scrutiny and regulation may emerge around the transparency and auditability of knowledge within large language models, especially those used in public-facing applications.
A public understanding of 'AI-induced opinion manipulation' could erode trust in AI-generated content, prompting a demand for verifiable, source-backed information.
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Read at arXiv cs.CL