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This paper introduces FLAS, a flow-based activation steering method that learns a concept-conditioned velocity field to steer language model activations at inference time. On the AxBench benchmark, FLAS is the first learned method to consistently outperform in-context prompting on held-out concepts without per-concept tuning.
OpenAI introduces Glow, an improved reversible generative model that simplifies the RealNVP architecture by replacing fixed permutations with learned 1x1 convolutions, enabling better information flow and significant performance improvements.