<ul><li>Language models need to be trustworthy and reliable as they become more powerful and sophisticated.</li><li>Researchers trained a Taboo model to elicit specific hidden knowledge not presented in the training data or prompt.</li><li>They evaluated non-interpretability approaches and developed mechanistic interpretability techniques like logit lens and sparse autoencoders to uncover the hidden knowledge.</li><li>The findings suggest promising avenues for future work in eliciting hidden knowledge from language models to ensure their safe deployment.</li></ul>

Towards eliciting latent knowledge from LLMs with mechanistic interpretability

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