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🔍 Today's pick in Interpretability & Analysis of LMs: x2 edition!
🔍 Today's pick in Interpretability & Analysis of LMs: x2 edition!
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Today's highlighted works aim reproduce findings from Transformer-centric interpretability literature on new RNN-based architectures such as Mamba and RWKV:
Today's highlighted works aim reproduce findings from Transformer-centric interpretability literature on new RNN-based architectures such as Mamba and RWKV:
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https://huggingface.co/papers/2404.05971
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2404.05971
https://huggingface.co/papers/2404.05971
Does Transformer Interpretability Transfer to RNNs? (2404.05971)
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@MrGonao
MrGonao
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 T. Marshall 
 T. Marshall 
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@norabelrose
norabelrose
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https://huggingface.co/papers/2404.03646
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2404.03646
https://huggingface.co/papers/2404.03646
Locating and Editing Factual Associations in Mamba (2404.03646)
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@sensharma
sensharma
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@datkinson
datkinson
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@davidbau
davidbau
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The first paper applies contrastive activation addition, the tuned lens and probing for eliciting latent knowledge in quirky models to Mamba and RWKV LMs, finding these Transformer-specific methods can be applied with slight adaptation to these architectures, obtaining similar results.
The first paper applies contrastive activation addition, the tuned lens and probing for eliciting latent knowledge in quirky models to Mamba and RWKV LMs, finding these Transformer-specific methods can be applied with slight adaptation to these architectures, obtaining similar results.
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The second work applies the ROME method to Mamba, finding weights playing the role of MLPs in encoding factual relations across several Mamba layers, and can be patched to perform model editing. A new SSM-specific technique is also introduced to emulate attention knockout (value zeroing) revealing information flows similar to the ones in Transformers when processing factual statements.
The second work applies the ROME method to Mamba, finding weights playing the role of MLPs in encoding factual relations across several Mamba layers, and can be patched to perform model editing. A new SSM-specific technique is also introduced to emulate attention knockout (value zeroing) revealing information flows similar to the ones in Transformers when processing factual statements.
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💻 Code: 
💻 Code: 
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https://github.com/arnab-api/romba
https://github.com/arnab-api/romba
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🔍 All daily picks: 
🔍 All daily picks: 
link
https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-ofc-lms-65ae3339949c5675d25de2f9
https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-ofc-lms-65ae3339949c5675d25de2f9
🔍 Today's pick in Interpretability & Analysis of LMs: x2 edition!

Today's highlighted works aim reproduce findings from Transformer-centric interpretability literature on new RNN-based architectures such as Mamba and RWKV:

https://huggingface.co/papers/2404.05971 by @MrGonao T. Marshall @norabelrose

https://huggingface.co/papers/2404.03646  by @sensharma @datkinson @davidbau

The first paper applies contrastive activation addition, the tuned lens and probing for eliciting latent knowledge in quirky models to Mamba and RWKV LMs, finding these Transformer-specific methods can be applied with slight adaptation to these architectures, obtaining similar results.

The second work applies the ROME method to Mamba, finding weights playing the role of MLPs in encoding factual relations across several Mamba layers, and can be patched to perform model editing. A new SSM-specific technique is also introduced to emulate attention knockout (value zeroing) revealing information flows similar to the ones in Transformers when processing factual statements.

💻 Code: https://github.com/arnab-api/romba

🔍 All daily picks: https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-ofc-lms-65ae3339949c5675d25de2f9
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samusenps osanseviero clem datkinson
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samusenps osanseviero clem datkinson