Censored LLMs as a Natural Testbed for Secret Knowledge Elicitation
This work provides a natural testbed for evaluating honesty elicitation and lie detection techniques, addressing the problem of naturally occurring dishonesty in LLMs for researchers and developers working on model safety and truthfulness. It is an incremental step in understanding and mitigating model censorship.
The paper investigates methods to extract suppressed information from Chinese LLMs (Qwen3) that are trained to censor politically sensitive topics. They found that sampling without a chat template, few-shot prompting, and fine-tuning on generic honesty data most reliably increased truthful responses, while prompting the censored model to classify its own responses performed well for lie detection. No technique fully eliminated false responses.
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 Falun Gong or the Tiananmen protests 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. Notably, no technique fully eliminates false responses. We release all prompts, code, and transcripts.