Nov 25, 2024

Sparse Autoencoders and Gemma 2-2B: Pioneering Demographic-Sensitive Language Modeling for Opinion QA

Qianmian Guo

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Summary

This project investigates the integration of Sparse Autoencoders (SAEs) with the gemma 2-2b lan- guage model to address challenges in opinion-based question answering (QA). Existing language models often produce answers reflecting narrow viewpoints, aligning disproportionately with specific demographics. By leveraging the Opinion QA dataset and introducing group-specific adjustments in the SAE’s latent space, this study aims to steer model outputs toward more diverse perspectives. The proposed framework minimizes reconstruction, sparsity, and KL divergence losses while maintaining interpretability and computational efficiency. Results demonstrate the feasibility of this approach for demographic-sensitive language modeling.

Cite this work:

@misc {

title={

Sparse Autoencoders and Gemma 2-2B: Pioneering Demographic-Sensitive Language Modeling for Opinion QA

},

author={

Qianmian Guo

},

date={

11/25/24

},

organization={Apart Research},

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

howpublished={https://apartresearch.com}

}

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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.