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Nov 25, 2024

Unveiling Latent Beliefs Using Sparse Autoencoders

Carlos Cortez, Eivind Otto Hjelle, Sanchit Kalhan

Details

Details

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Language models (LMs) often generate outputs that are linguistically plausible yet factually incorrect, raising questions about their internal representations of truth and belief. This paper explores the use of sparse autoencoders (SAEs) to identify and manipulate features

that encode the model’s confidence or belief in the truth of its answers. Using GoodFire

AI’s powerful API tools of semantic and contrastive search methods, we uncover latent fea-

tures associated with correctness and accuracy in model responses. Experiments reveal that certain features can distinguish between true and false statements, while others serve as controls to validate our approach. By steering these belief-associated features, we demonstrate the ability to influence model behavior in a targeted manner, improving or degrading factual accuracy. These findings have implications for interpretability, model alignment, and enhancing the reliability of AI systems.

Cite this work:

@misc {

title={

@misc {

},

author={

Carlos Cortez, Eivind Otto Hjelle, Sanchit Kalhan

},

date={

11/25/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Reviewer's Comments

Reviewer's Comments

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Simon Lermen

In the introduction, they talk about anecdotal evidence for a quite specific behavior, which I have never heard of: "Anecdotal evidence suggests that LLMs are more likely to correct information in common domains of knowledge that are well represented in the training data, whereas they are more likely to apologize for being wrong when it comes to more niche domains of knowledge or specific problems." I think models clearly understand the concept of a degree of belief, but it might be more accurate to simply ask them about it directly. I conducted a small experiment asking ChatGPT which seemed more likely: the moon landings being fake or the Earth being flat. It decided that "the moon landings being fake" is more likely, which I believe is true. They also conducted an experiment steering a model while it solved arithmetic questions to make it produce false results. They experimented with a few features and included a control feature; however, it appears they never returned to this and didn't show results for the control feature. The main plot shows a straight line for one of the features, which could be the private feature, but there is no labeling or legend for the different features. In general, the title seems aspirational—it makes sense that they could only explore the idea superficially as part of this hackathon. For the most part, it appears they just identified features related to truthfulness, manipulated them in both directions, and evaluated the results on a dataset. It would be interesting to identify more specific features for belief, such as changing how certain the model is in certain areas, rather than simply whether its outputs are correct or false. They also haven't yet uncovered any hidden beliefs in the model, as the title suggests. This could be fascinating—for example, does the model give medical advice while being uncertain? Could belief steering be used to make the model doubt itself more or be more cautious in high-stakes areas?

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Our solution creates an open-source standards framework that enables collaboration and knowledge-sharing between frontier AI safety teams while protecting intellectual property and addressing antitrust concerns. By operating as an OASIS Open Project, we can provide legal protection for industry cooperation on developing integrated standards for risk management and monitoring.

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The commercial viability of our solution is evidenced by strong willingness-to-pay across all major stakeholder groups for quality and conformity tools. By reducing duplication of effort in standards development and enabling economies of scale in implementation, we create clear value for participants while advancing the critical goal of AI safety.

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.