arXiv Machine Learning

High-Dimensional Random Projection for Activation Steering in Language Models

arXiv:2606. 15092v1 Announce Type: new Abstract: Activation steering has emerged as a key methodology for controlling the behavior of large language models (LLMs).

arXiv Machine Learning

Jun 9

Beyond Linear Activation Steering: Invertible Latent Transformations for Controlling LLM Behavior

arXiv:2606. 08454v1 Announce Type: new Abstract: Activation steering provides a lightweight inference-time mechanism for controlling large language models (LLMs) by modifying their internal activation vectors toward desired behaviors.

By Tuc Nguyen, Thai Le

llms diffusion benchmarks safety

arXiv AI

Jun 11

Neural FOXP2 -- Language Specific Neuron Steering for Targeted Language Improvement in LLMs

arXiv:2602. 00945v2 Announce Type: replace-cross Abstract: LLMs are multilingual by training, yet their lingua franca is often English, reflecting English language dominance in pretraining.

By Anusa Saha, Tanmay Joshi, Vinija Jain, Aman Chadha, Amitava Das

llms

arXiv Machine Learning

Jun 15

Towards Steering without Sacrifice: Principled Training of Steering Vectors for Prompt-only Interventions

arXiv:2605. 05983v2 Announce Type: replace Abstract: Recently, steering vectors (SVs) have emerged as an effective and lightweight approach to steer behaviors of large language models (LLMs), among which fine-tuned SVs are more effective than optimization-free ones.

By Yuntai Bao, Qinfeng Li, Xinyan Yu, Ge Su, Wenqi Zhang, Liu Yan, Haiqin Weng, Jianwei Yin, Xuhong Zhang

llms fine-tuning safety

arXiv Machine Learning

Jul 9

Distributed Sparse Interventions in Language Models

arXiv:2607. 07128v1 Announce Type: new Abstract: Language models perform a wide range of tasks at varying levels of abstraction with the capacity to flexibly infer tasks from context, execute multiple tasks simultaneously, and select among competing tasks.

By Maximilian S. Ernst (Max Planck School of Cognition, Center for Lifespan Psychology Max Planck Institute for Human Development, Machine Learning Group Technische Universit\"at Berlin), Lorenz Linhardt (Machine Learning Group Technische Universit\"at Berlin, Berlin Institute for the Foundations of Learning and Data), Aaron Peikert (Center for Lifespan Psychology Max Planck Institute for Human Development), Oliver Eberle (Machine Learning Group Technische Universit\"at Berlin, Berlin Institute for the Foundations of Learning and Data)

llms

arXiv AI

Jul 13

GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs

arXiv:2507. 18043v2 Announce Type: replace-cross Abstract: Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at test time without updating model weights.

By Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin, Mohit Bansal

llms fine-tuning multimodal safety

arXiv Machine Learning

Jun 11

When is Your LLM Steerable?

arXiv:2606. 11599v1 Announce Type: cross Abstract: Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, model, and steering configuration.

By Chenrui Fan, Yize Cheng, Ming Li, Soheil Feizi, Tianyi Zhou

llms

arXiv Machine Learning

Jun 4

STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations

arXiv:2606. 05165v1 Announce Type: new Abstract: Training Data Attribution (TDA) seeks to trace a model's predictions back to its training data.

By Rishit Dagli, Abir Harrasse, Luke Zhang, Florent Draye, Amirali Abdullah, Bernhard Sch\"olkopf, Zhijing Jin

llms benchmarks

arXiv Machine Learning

Aug 4

LangFIR: Discovering Sparse Language-Specific Features from Monolingual Data for Language Steering

arXiv:2604. 03532v2 Announce Type: replace-cross Abstract: Large language models (LLMs) show strong multilingual capabilities, yet reliably controlling the language of their outputs remains difficult.

By Sing Hieng Wong, Hassan Sajjad, A. B. Siddique

llms benchmarks

arXiv AI

Jun 9

Adversarial Robustness of Activation Steering in Large Language Models

arXiv:2606. 07696v1 Announce Type: cross Abstract: Activation steering has become a popular training-free method to control LLM behavior by injecting precomputed direction vectors into the model's residual stream at inference time.

By Kien Le, Thai Le

llms safety

arXiv Machine Learning

Aug 7

CircuitSteer: Geometrically Aligned Multi-Layer Steering via Sparse Autoencoder Circuits

arXiv:2608. 05732v1 Announce Type: new Abstract: Controlling the behavior of large language models (LLMs) remains a critical challenge for AI alignment.

By Mehrshad Saadatinia, Parsa Razmara, Ardalan Aryashad, Ali Abbasi, Seyedarmin Azizi

llms safety

arXiv AI

Jul 7

InverseScope: Scalable Activation Inversion for Interpreting Large Language Models

arXiv:2506. 07406v3 Announce Type: replace-cross Abstract: Understanding the internal representations of large language models (LLMs) is a central challenge in interpretability research.

By Yifan Luo, Zhennan Zhou, Bin Dong

llms efficiency benchmarks safety

Hugging Face Trending Papers

Jun 10

When is Your LLM Steerable?

Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, model, and steering configuration. Finding the regime and boundaries of successful steering typically requires expensive grid searches and post-hoc evaluation of full autoregressive rollouts.

llms

Related stories