Censored LLMs as a Natural Testbed for Secret Knowledge Elicitation

This paper was either an anonymous submission of interesting research or was written by a student; full credit remains with the author(s), linked below. Large language models sometimes produce false or misleading responses. Two approaches to this problem are honesty elicitation—modifying prompts or weights so that the model answers truthfully—and lie detection—classifying whether a given response is false. Prior work evaluates such methods on models specifically trained to lie or conceal information, but these artificial constructions may not resemble naturally-occurring dishonesty. We instead study open-weights LLMs from Chinese developers, which are trained to censor politically sensitive topics: Qwen3 models frequently produce falsehoods about subjects like the Tiananmen protests or the COVID-19 outbreak while occasionally answering correctly, indicating they possess knowledge they are trained to suppress. Using this as a testbed, we evaluate a suite of elicitation and lie detection techniques. For honesty elicitation, sampling without a chat template, few-shot prompting, and fine-tuning on generic honesty data most reliably increase truthful responses. For lie detection, prompting the censored model to classify its own responses performs near an uncensored-model upper bound, and linear probes trained on unrelated data offer a cheaper alternative. The strongest honesty elicitation techniques also transfer to frontier open-weights models, including DeepSeek-R1 and Qwen3.5-397B. Notably, no technique fully eliminates false responses. We release all prompts, data, and code.