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%20were%20fathomable%20and%20contained%20human-understandable%20concepts.%20Yet%2C%20these%20methods%20are%20seldom%20contextualised%20and%20are%20often%20based%20on%20a%20single%20hidden%20state%2C%20which%20makes%20them%20unable%20to%20interpret%20multi-step%20reasoning%2C%20e.g.%20planning.%20In%20this%20respect%2C%20we%20propose%20contrastive%20sparse%20autoencoders%20(CSAE)%2C%20a%20novel%20framework%20for%20studying%20pairs%20of%20game%20trajectories.%20Using%20CSAE%2C%20we%20are%20able%20to%20extract%20and%20interpret%20concepts%20that%20are%20meaningful%20to%20the%20chess-agent%20plans.%20We%20primarily%20focused%20on%20a%20qualitative%20analysis%20of%20the%20CSAE%20features%20before%20proposing%20an%20automated%20feature%20taxonomy.%20Furthermore%2C%20to%20evaluate%20the%20quality%20of%20our%20trained%20CSAE%2C%20we%20devise%20sanity%20checks%20to%20wave%20spurious%20correlations%20in%20our%20results.)












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![[논문 리뷰] SAeUron: Interpretable Concept Unlearning in Diffusion Models ...](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/saeuron-interpretable-concept-unlearning-in-diffusion-models-with-sparse-autoencoders-0.png)












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