Stanford NLP Python library for benchmarking the utility of LLM interpretability methods
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Stanford NLP Python library for benchmarking the utility of LLM interpretability methods
[ICLR 2025] General-purpose activation steering library
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Early baby steps towards a long-term vision regarding Mamba-2's state interpretability.
A closed-loop control system for Large Language Models that steers internal activation states in real-time to prevent mode collapse and toxicity
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