Executive summary: The post announces open summer MATS applications and outlines several exciting research directions in mechanistic interpretability, including understanding thinking models, advancing sparse autoencoders, exploring model diffing, investigating safety-relevant behaviors, promoting practical interpretability projects, and examining fundamental assumptions in the field.
Key points:
Summer MATS applications are now open for supervising mechanistic interpretability projects, with a submission deadline of February 28.
Interest in studying thinking models that generate extensive chains of thought to unravel their reasoning processes and assess their determinism and safety.
Continued focus on Sparse Autoencoders (SAEs) to identify and address fundamental issues, improve interpretability techniques, and explore alternative decomposition methods.
Exploration of model diffing to understand changes during finetuning, which could provide insights into alignment and model behavior modifications.
Investigation of sophisticated and safety-relevant behaviors in large language models, such as alignment faking and user attribute modeling, highlighting the need for advanced interpretability tools.
Promotion of practical interpretability projects that tackle real-world tasks and challenge existing baselines, alongside a critical examination of foundational assumptions in mechanistic interpretability.
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Executive summary: The post announces open summer MATS applications and outlines several exciting research directions in mechanistic interpretability, including understanding thinking models, advancing sparse autoencoders, exploring model diffing, investigating safety-relevant behaviors, promoting practical interpretability projects, and examining fundamental assumptions in the field.
Key points:
Summer MATS applications are now open for supervising mechanistic interpretability projects, with a submission deadline of February 28.
Interest in studying thinking models that generate extensive chains of thought to unravel their reasoning processes and assess their determinism and safety.
Continued focus on Sparse Autoencoders (SAEs) to identify and address fundamental issues, improve interpretability techniques, and explore alternative decomposition methods.
Exploration of model diffing to understand changes during finetuning, which could provide insights into alignment and model behavior modifications.
Investigation of sophisticated and safety-relevant behaviors in large language models, such as alignment faking and user attribute modeling, highlighting the need for advanced interpretability tools.
Promotion of practical interpretability projects that tackle real-world tasks and challenge existing baselines, alongside a critical examination of foundational assumptions in mechanistic interpretability.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.