@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://aclanthology.org/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}."
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<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 https://github.com/IBM/AISteer360.</abstract>
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%0 Conference Proceedings
%T AI Steerability 360: A Toolkit for Steering Large Language Models
%A Miehling, Erik
%A Natesan Ramamurthy, Karthikeyan
%A Venkateswaran, Praveen
%A Ko, Ching-Yun
%A Dognin, Pierre
%A Singh, Moninder
%A Pedapati, Tejaswini
%A Balakrishnan, Avinash
%A Riemer, Matthew
%A Wei, Dennis
%A Vejsbjerg, Inge
%A Daly, Elizabeth M.
%A Varshney, Kush R.
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F miehling-etal-2026-ai
%X 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 https://github.com/IBM/AISteer360.
%U https://aclanthology.org/2026.acl-demo.43/
%P 436-444
Markdown (Informal)
[AI Steerability 360: A Toolkit for Steering Large Language Models](https://aclanthology.org/2026.acl-demo.43/) (Miehling et al., ACL 2026)
ACL
- Erik Miehling, Karthikeyan Natesan Ramamurthy, Praveen Venkateswaran, Ching-Yun Ko, Pierre Dognin, Moninder Singh, Tejaswini Pedapati, Avinash Balakrishnan, Matthew Riemer, Dennis Wei, Inge Vejsbjerg, Elizabeth M. Daly, and Kush R. Varshney. 2026. AI Steerability 360: A Toolkit for Steering Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 436–444, San Diego, California, United States. Association for Computational Linguistics.