@inproceedings{miehling-etal-2026-ai,
    title = "{AI} Steerability 360: A Toolkit for Steering Large Language Models",
    author = "Miehling, Erik  and
      Natesan Ramamurthy, Karthikeyan  and
      Venkateswaran, Praveen  and
      Ko, Ching-Yun  and
      Dognin, Pierre  and
      Singh, Moninder  and
      Pedapati, Tejaswini  and
      Balakrishnan, Avinash  and
      Riemer, Matthew  and
      Wei, Dennis  and
      Vejsbjerg, Inge  and
      Daly, Elizabeth M.  and
      Varshney, Kush R.",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.43/",
    pages = "436--444",
    ISBN = "979-8-89176-392-0",
    abstract = "The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. Steering abstractions are designed around four model control surfaces: input (modification of the prompt), structural (modification of the model{'}s weights or architecture), state (modification of the model{'}s activations and attentions), and output (modification of the decoding or generation process). Steering methods exert control on the model through a common interface, termed a steering pipeline, which additionally allows for the composition of multiple steering methods. Comprehensive evaluation and comparison of steering methods/pipelines is facilitated by use case classes (for defining tasks) and a benchmark class (for performance comparison on a given task). The functionality provided by the toolkit significantly lowers the barrier to developing and comprehensively evaluating steering methods. The toolkit is Hugging Face native and is released under an Apache 2.0 license at \url{https://github.com/IBM/AISteer360}."
}