Understanding Content Moderation in Large Language Models through Restricted Books: From Refusal to Warning

A recent study investigated how large language models (LLMs) moderate content, particularly concerning sensitive topics. The research utilized a controlled experiment involving 40,800 query-response pairs across 400 books, 17 prompt designs, and six frontier LLMs from various providers. The study compared responses to content from books formally challenged for restriction by the American Library Association against unrestricted books to understand LLM behavior from refusal to providing warnings regarding sensitive materials.

• The study systematically assessed content moderation in six frontier large language models using a controlled experiment design.
• Restricted books, defined as those formally challenged for removal or access restriction by the American Library Association (2000-2023), served as the primary testbed for sensitive topics.
• The research involved a substantial dataset of 40,800 query-response pairs and 400 books to evaluate model behavior.
• Seventeen distinct prompt designs were used to explore varying user interactions and their impact on LLM responses.
• The focus was on understanding the spectrum of LLM responses to sensitive content, ranging from outright refusal to providing warnings or other forms of moderation.

Reported by arXiv — Computers and Society · International. This briefing summarises the publication for executive use; the document itself is not reproduced here.