arXiv AI By Zach Studdiford, Timothy T. Rogers, Siddharth Suresh, Kushin Mukherjee

Evaluating Steering Techniques using Human Similarity Judgments

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arXiv AI
Sep 10

Steering Geometry: Validating Human Value Geometry in LLM Steering Space

arXiv:2609.06289v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a lightweight, inferenc...

By Mohammad Mahdi Abootorabi, Armin Saghafian, Ali Bazshoushtari, Hamid Rezaei, EunJeong Hwang, Vered Shwartz, Parvin Mousavi, Purang Abolmaesumi
arXiv Computation and Language
Sep 30

Cognitive Expert Language Models Better Align with the Corresponding Brain Systems

The study investigates whether language models tailored to specific cognitive domains better align with corresponding brain systems. By prompting and fine‑tuning large language models into six domain experts—sensory, spatial, numerical, reasoning, social, and abstract—the authors find that each expert’s representations more closely match the brain region associated with its domain than other experts. This domain‑specific alignment holds across multiple base models and fMRI datasets, while overall prediction accuracy remains largely unchanged, indicating that regional alignment can be obscured when summarizing across the brain.

By Zhivar Sourati, Mengxuan Helen Wu, Nona Ghazizadeh, Jonas Kaplan, Morteza Dehghani, Samuel A. Nastase
arXiv Machine Learning
Jul 9

Towards Understanding Steering Strength

arXiv:2602. 02712v2 Announce Type: replace Abstract: A popular approach to post-training control of large language models (LLMs) is the steering of intermediate latent representations.

By Magamed Taimeskhanov, Samuel Vaiter, Damien Garreau